NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo.

The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.

Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy.

The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The motoneuronal control provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decodes the subject's intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research.

Larger and Denser: An Optimal Design for Surface Grids of EMG Electrodes to Identify Greater and More Representative Samples of Motor Units.

The spinal motor neurons are the only neural cells whose individual activity can be noninvasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IEDs). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56 ± 14; range: 39-79) and the percentage of early recruited motor units within these samples (29 ± 14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies.

The History and Future of Neuromusculoskeletal Biomechanics.

The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.

Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets.

Deep learning biomechanical models perform optimally when trained with large datasets, however these can be challenging to collect in gait labs, while limited augmentation techniques are available. This study presents a data augmentation approach based on generative adversarial networks which generate synthetic motion capture (mocap) datasets of marker trajectories and ground reaction forces (GRFs). The proposed architecture, called adversarial autoencoder, consists of an encoder compressing mocap data to a latent vector, a decoder reconstructing the mocap data from the latent vector and a discriminator distinguishing random vectors from encoded latent vectors. Direct kinematics (DK) and inverse kinematics (IK) joint angles, GRFs, and inverse dynamics (ID) joint moments calculated for real and synthetic trials were compared using statistical parametric mapping to assure realistic data generation and select optimal architectural hyperparameters based on percentage average differences across the gait cycle length. We observed negligible differences for DK computed joint angles and GRFs, but not for inverse methods (IK: 29.2%, ID: 35.5%). When the same architecture was trained also including the joint angles calculated by IK, we found no significant differences in the kinematics and GRFs, and improvements in joint moments estimation (ID: 25.7%). Finally, we showed that our data augmentation approach improved the accuracy of joint kinematics (up to 23%, 0.8°) and vertical GRFs (11%) predicted by standard neural networks using a single simulated pelvic inertial measurement unit. These findings suggest that predictive deep learning models can benefit from the synthetic datasets produced with the proposed technique.

Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling.

Our understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to electrophysiological findings from animal experiments extrapolated to humans, mathematical models of MN pools not validated for human data, and experimental results obtained from decomposition of electromyographical (EMG) signals. These approaches are limited in accuracy or provide information on only small partitions of the MN population. Here, we propose a method based on the combination of high-density EMG (HDEMG) data and realistic modelling for predicting the behaviour of entire pools of motoneurons in humans. The method builds on a physiologically realistic model of a MN pool which predicts, from the experimental spike trains of a smaller number of individual MNs identified from decomposed HDEMG signals, the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. The MN pool model is described as a cohort of single-compartment leaky fire-and-integrate (LIF) models of MNs scaled by a physiologically realistic distribution of MN electrophysiological properties and driven by a spinal synaptic input, both derived from decomposed HDEMG data. The MN spike trains and effective neural drive to muscle, predicted with this method, have been successfully validated experimentally. A representative application of the method in MN-driven neuromuscular modelling is also presented. The proposed approach provides a validated tool for neuroscientists, experimentalists, and modelers to infer the firing activity of MNs that cannot be observed experimentally, investigate the neuromechanics of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.

Mathematical relationships between spinal motoneuron properties.

Our understanding of the behaviour of spinal alpha-motoneurons (MNs) in mammals partly relies on our knowledge of the relationships between MN membrane properties, such as MN size, resistance, rheobase, capacitance, time constant, axonal conduction velocity, and afterhyperpolarization duration. We reprocessed the data from 40 experimental studies in adult cat, rat, and mouse MN preparations to empirically derive a set of quantitative mathematical relationships between these MN electrophysiological and anatomical properties. This validated mathematical framework, which supports past findings that the MN membrane properties are all related to each other and clarifies the nature of their associations, is besides consistent with the Henneman's size principle and Rall's cable theory. The derived mathematical relationships provide a convenient tool for neuroscientists and experimenters to complete experimental datasets, explore the relationships between pairs of MN properties never concurrently observed in previous experiments, or investigate inter-mammalian-species variations in MN membrane properties. Using this mathematical framework, modellers can build profiles of inter-consistent MN-specific properties to scale pools of MN models, with consequences on the accuracy and the interpretability of the simulations.

Muscles receive their instructions through electrical signals carried by tens or hundreds of cells connected to the command centers of the body. These 'alpha-motoneurons' have various sizes and electrical characteristics which affect how they transmit signals. Previous experiments have shown that these properties are linked; for instance, larger motoneurons transfer electrical signals more quickly. The exact nature of the mathematical relationships between these characteristics, however, remains unclear. This limits our understanding of the behaviour of motoneurons from experimental data. To identify the equations linking eight motoneuron properties, Caillet et al. analysed published datasets from experimental studies on cat motoneurons. This approach uncovered simple mathematical associations: in fact, only one characteristic needs to be measured experimentally to calculate all the other properties. The relationships identified were also consistent with previously accepted approaches for modelling motoneuron activity. Caillet et al. then validated this mathematical framework with data from studies on rodents, showing that some of the equations hold true for different mammals. This work offers a quick and easy way for researchers to calculate the characteristics of a motoneuron based on a single observation. This will allow non-measured properties to be added to experimental datasets, and it could help to uncover the diversity of motoneurons at work within a population.

Altering the strength of the muscles crossing the lower limb joints only affects knee joint reaction forces.

BACKGROUND: Generic musculoskeletal models based on literature data are often used to estimate joint reaction forces (JRFs) that otherwise could only be measured invasively. Estimated JRFs are sensitive to changes in maximum isometric force (Fiso) of the muscles, but these are normally simply scaled using a multiplicative coefficient. The impact of varying Fiso, or strength, of muscles crossing each lower limb joint on estimated JRFs has not been systematically explored in musculoskeletal models of the lower limb. RESEARCH QUESTION: How do alterations in the strength of joint-crossing muscles influence the lower limb JRF magnitudes computed through a generic musculoskeletal model? METHODS: By modifying Fiso of muscles crossing hip, knee, ankle, or all joints at once up to ± 40% in 10% increments, thirty-two models were created to simulate the gait of a patient with an instrumented tibial prosthesis (5th Grand Challenge dataset). A standard workflow (inverse kinematics, static optimization, joint reaction analysis) was utilized to calculate JRFs. Both alterations in JRF magnitudes due to joint crossing muscles' strength modifications and their accuracy against in vivo knee loading measurements were quantified. RESULTS: The knee JRF was the most sensitive force to changes in the joint-crossing muscles' strength (variations ranging from -37.9 ± 0.5% to +37.9 ± 3.2%), while the hip and ankle JRFs were almost unaffected (maximum variation: +6.1%). Reducing the strength of knee and ankle-crossing muscles and intensifying the strength of hip-crossing muscles lowered the knee JRF. The knee JRF was best estimated (peak error: 0.42 ± 0.15 body weight, root mean squared error: 0.37 ± 0.06 body weight, coefficient of determination: 0.76 ± 0.10) by the model with -40% weakened knee-crossing muscles. SIGNIFICANCE: Altering strengths mainly affects knee JRF estimated with generic musculoskeletal models, suggesting that personalization of strength of joint-crossing muscles is required for accurate knee JRF estimations. Rehabilitation regimes meant to strengthen muscles crossing a joint should be carefully designed to avoid undesired effects on the other joints.

Using Musculoskeletal Models to Estimate in vivo Total Knee Replacement Kinematics and Loads: Effect of Differences Between Models.

Total knee replacement (TKR) is one of the most performed orthopedic surgeries to treat knee joint diseases in the elderly population. Although the survivorship of knee implants may extend beyond two decades, the poor outcome rate remains considerable. A recent computational approach used to better understand failure modes and improve TKR outcomes is based on the combination of musculoskeletal (MSK) and finite element models. This combined multiscale modeling approach is a promising strategy in the field of computational biomechanics; however, some critical aspects need to be investigated. In particular, the identification and quantification of the uncertainties related to the boundary conditions used as inputs to the finite element model due to a different definition of the MSK model are crucial. Therefore, the aim of this study is to investigate this problem, which is relevant for the model credibility assessment process. Three different generic MSK models available in the OpenSim platform were used to simulate gait, based on the experimental data from the fifth edition of the "Grand Challenge Competitions to Predict in vivo Knee Loads." The outputs of the MSK analyses were compared in terms of relative kinematics of the knee implant components and joint reaction (JR) forces and moments acting on the tibial insert. Additionally, the estimated knee JRs were compared with those measured by the instrumented knee implant so that the "global goodness of fit" was quantified for each model. Our results indicated that the different kinematic definitions of the knee joint and the muscle model implemented in the different MSK models influenced both the motion and the load history of the artificial joint. This study demonstrates the importance of examining the influence of the model assumptions on the output results and represents the first step for future studies that will investigate how the uncertainties in the MSK models propagate on disease-specific finite element model results.

Dependency of lower limb joint reaction forces on femoral version.

BACKGROUND: Musculoskeletal (MSK) models based on literature data are meant to represent a generic anatomy and are a popular tool employed by biomechanists to estimate the internal loads occurring in the lower limb joints, such as joint reaction forces (JRFs). However, since these models are normally just linearly scaled to an individual's anthropometry, it is unclear how their estimations would be affected by the personalization of key features of the MSK anatomy, one of which is the femoral version angle. RESEARCH QUESTION: How are the lower limb JRF magnitudes computed through a generic MSK model affected by changes in the femoral version? METHODS: We developed a bone-deformation tool in MATLAB (shared at https://simtk.org/projects/bone_deformity) and used it to create a set of seven OpenSim models spanning from 2˚ femoral retroversion to 40˚ anteversion. We used these models to simulate the gait of an elderly individual with an instrumented prosthesis implanted at their knee joint (5th Grand Challenge dataset) and quantified both the changes in JRFs magnitude due to varying the skeletal anatomy and their accuracy against the correspondent in vivo measurements at the knee joint. RESULTS: Hip and knee JRF magnitudes were affected by the femoral version with variations from the unmodified generic model up to 17.9 ±â€¯4.5% at the hip and 43.4 ±â€¯27.1% at the knee joint. The ankle joint was unaffected by the femoral geometry. The MSK models providing the most accurate knee JRFs (root mean squared error: 0.370 ±â€¯0.068 body weight, coefficient of determination: 0.757 ±â€¯0.104, peak error range: 0.09-0.42 body weight) were those with femoral anteversion angle closer to that measured on the segmented bone of the individual. SIGNIFICANCE: Femoral version substantially affects hip and knee JRFs estimated with generic MSK models, suggesting that personalizing key MSK anatomical features might be necessary for accurate estimation of JRFs with these models.

Bisect offset ratio and cartilaginous sulcus angle are good combined predictors of recurrent patellar dislocation in children and adolescents.

OBJECTIVES: Recurrent patellar dislocation (RPD) is found most commonly in the juvenile population. While risk factors have been well-established in adults, there remains a paucity in radiographical data to define normal and pathoanatomical juvenile cohorts. The objectives of this paper were to elucidate the differences in the patellofemoral joint between RPD and typically developed (TD) juvenile populations, using MRI measurements, and determine the best independent and combined predictors of RPD. METHODS: A prospective, cross-sectional study was conducted with 25 RPD and 24 TD participants aged between 8 and 19 years. MR images were obtained to assess common measures of lower limb alignment, patellofemoral alignment, and trochlear dysplasia. RESULTS: Significant differences were evident for acetabular inclination, tibial-femoral torsion, tibial tubercle-to-trochlear groove (TT-TG) distance, lateral patellar tilt (LPT), cartilaginous sulcus angle (CSA) and bisect offset ratio (BOR). CSA and BOR were included in the final predictive model, which correctly classified 89.4% of RPD cases. CONCLUSION: Radiographical parameters that stratify risk of RPD in adults are also able to predict RPD in the pediatric population (TT-TG, LPT, CSA and BOR). Together, CSA and BOR accurately identified 89.4% of RPD. These measures should be included in the evaluation of pediatric patients who present with patellar dislocation. LEVEL OF EVIDENCE: Level II.

Automatic generation of personalised skeletal models of the lower limb from three-dimensional bone geometries.

The generation of personalised and patient-specific musculoskeletal models is currently a cumbersome and time-consuming task that normally requires several processing hours and trained operators. We believe that this aspect discourages the use of computational models even when appropriate data are available and personalised biomechanical analysis would be beneficial. In this paper we present a computational tool that enables the fully automatic generation of skeletal models of the lower limb from three-dimensional bone geometries, normally obtained by segmentation of medical images. This tool was evaluated against four manually created lower limb models finding remarkable agreement in the computed joint parameters, well within human operator repeatability. The coordinate systems origins were identified with maximum differences between 0.5 mm (hip joint) and 5.9 mm (subtalar joint), while the joint axes presented discrepancies between 1° (knee joint) to 11° (subtalar joint). To prove the robustness of the methodology, the models were built from four datasets including both genders, anatomies ranging from juvenile to elderly and bone geometries reconstructed from high-quality computed tomography as well as lower-quality magnetic resonance imaging scans. The entire workflow, implemented in MATLAB scripting language, executed in seconds and required no operator intervention, creating lower extremity models ready to use for kinematic and kinetic analysis or as baselines for more advanced musculoskeletal modelling approaches, of which we provide some practical examples. We auspicate that this technical advancement, together with upcoming progress in medical image segmentation techniques, will promote the use of personalised models in larger-scale studies than those hitherto undertaken.

Automated Generation of Three-Dimensional Complex Muscle Geometries for Use in Personalised Musculoskeletal Models.

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.

An extended discrete element method for the estimation of contact pressure at the ankle joint during stance phase.

Abnormalities in the ankle contact pressure are related to the onset of osteoarthritis. In vivo measurements are not possible with currently available techniques, so computational methods such as the finite element analysis (FEA) are often used instead. The discrete element method (DEM), a computationally efficient alternative to time-consuming FEA, has also been used to predict the joint contact pressure. It describes the articular cartilage as a bed of independent springs, assuming a linearly elastic behaviour and absence of relative motion between the bones. In this study, we present the extended DEM (EDEM) which is able to track the motion of talus over time. The method was used, with input data from a subject-specific musculoskeletal model, to predict the contact pressure in the ankle joint during gait. Results from EDEM were also compared with outputs from conventional DEM. Predicted values of contact area were larger in EDEM than they were in DEM (4.67 and 4.18 cm2, respectively). Peak values of contact pressure, attained at the toe-off, were 7.3 MPa for EDEM and 6.92 MPa for DEM. Values predicted from EDEM fell well within the ranges reported in the literature. Overall, the motion of the talus had more effect on the extension and shape of the pressure distribution than it had on the magnitude of the pressure. The results indicated that EDEM is a valid methodology for the prediction of ankle contact pressure during daily activities.

Muscle recruitment strategies can reduce joint loading during level walking.

Joint inflammation, with consequent cartilage damage and pain, typically reduces functionality and affects activities of daily life in a variety of musculoskeletal diseases. Since mechanical loading is an important determinant of the disease process, a possible conservative treatment is the unloading of joints. In principle, a neuromuscular rehabilitation program aimed to promote alternative muscle recruitments could reduce the loads on the lower-limb joints during walking. The extent of joint load reduction one could expect from this approach remains unknown. Furthermore, assuming significant reductions of the load on the affected joint can be achieved, it is unclear whether, and to what extent, the other joints will be overloaded. Using subject-specific musculoskeletal models of four different participants, we computed the muscle recruitment strategies that minimised the hip, knee and ankle contact force, and predicted the contact forces such strategies induced at the other joints. Significant reductions of the peak force and impulse at the knee and hip were obtained, while only a minimal effect was found at the ankle joint. Adversely, the peak force and the impulse in non-targeted joints increased when aiming to minimize the load in an adjacent joint. These results confirm the potential of alternative muscle recruitment strategies to reduce the loading at the knee and the hip, but not at the ankle. Therefore, neuromuscular rehabilitation can be targeted to reduce the loading at affected joints but must be considered carefully in patients with multiple joints affected due to the potential adverse effects in non-targeted joints.

Development and validation of subject-specific pediatric multibody knee kinematic models with ligamentous constraints.

Computational knee models that replicate the joint motion are important tools to discern difficult-to-measure functional joint biomechanics. Numerous knee kinematic models of different complexity, with either generic or subject-specific anatomy, have been presented and used to predict three-dimensional tibiofemoral (TFJ) and patellofemoral (PFJ) joint kinematics of cadavers or healthy adults, but not pediatric populations. The aims of this study were: (i) to develop subject-specific TFJ and PFJ kinematic models, with TFJ models having either rigid or extensible ligament constraints, for eight healthy pediatric participants and (ii) to validate the estimated joint and ligament kinematics against in vivo kinematics measured from magnetic resonance imaging (MRI) at four TFJ flexion angles. Three different TFJ models were created from MRIs and used to solve the TFJ kinematics: (i) 5-rigid-link parallel mechanism with rigid surface contact and isometric anterior cruciate (ACL), posterior cruciate (PCL) and medial collateral (MCL) ligaments (ΔLnull), (ii) 6-link parallel mechanism with minimized ACL, PCL, MCL and lateral collateral ligament (LCL) length changes (ΔLmin) and (iii) 6-link parallel mechanism with prescribed ACL, PCL, MCL and LCL length variations (ΔLmatch). Each model's geometrical parameters were optimized using a Multiple Objective Particle Swarm algorithm. When compared to MRI-measured data, ΔLnull and ΔLmatch performed the best, with average root mean square errors below 6.93° and 4.23 mm for TFJ and PFJ angles and displacements, respectively, and below 2.01 mm for ligament lengths (<4.32% ligament strain). Therefore, within these error ranges, ΔLnull and ΔLmatch can be used to estimate three-dimensional pediatric TFJ, PFJ and ligament kinematics and can be incorporated into lower-limb models to estimate joint kinematics and kinetics during dynamic tasks.

Linking Joint Impairment and Gait Biomechanics in Patients with Juvenile Idiopathic Arthritis.

Juvenile Idiopathic Arthritis (JIA) is a paediatric musculoskeletal disease of unknown aetiology, leading to walking alterations when the lower-limb joints are involved. Diagnosis of JIA is mostly clinical. Imaging can quantify impairments associated to inflammation and joint damage. However, treatment planning could be better supported using dynamic information, such as joint contact forces (JCFs). To this purpose, we used a musculoskeletal model to predict JCFs and investigate how JCFs varied as a result of joint impairment in eighteen children with JIA. Gait analysis data and magnetic resonance images (MRI) were used to develop patient-specific lower-limb musculoskeletal models, which were evaluated for operator-dependent variability (< 3.6°, 0.05 N kg-1 and 0.5 BW for joint angles, moments, and JCFs, respectively). Gait alterations and JCF patterns showed high between-subjects variability reflecting the pathology heterogeneity in the cohort. Higher joint impairment, assessed with MRI-based evaluation, was weakly associated to overall joint overloading. A stronger correlation was observed between impairment of one limb and overload of the contralateral limb, suggesting risky compensatory strategies being adopted, especially at the knee level. This suggests that knee overloading during gait might be a good predictor of disease progression and gait biomechanics should be used to inform treatment planning.

An image-based kinematic model of the tibiotalar and subtalar joints and its application to gait analysis in children with Juvenile Idiopathic Arthritis.

In vivo estimates of tibiotalar and the subtalar joint kinematics can unveil unique information about gait biomechanics, especially in the presence of musculoskeletal disorders affecting the foot and ankle complex. Previous literature investigated the ankle kinematics on ex vivo data sets, but little has been reported for natural walking, and even less for pathological and juvenile populations. This paper proposes an MRI-based morphological fitting methodology for the personalised definition of the tibiotalar and the subtalar joint axes during gait, and investigated its application to characterise the ankle kinematics in twenty patients affected by Juvenile Idiopathic Arthritis (JIA). The estimated joint axes were in line with in vivo and ex vivo literature data and joint kinematics variation subsequent to inter-operator variability was in the order of 1°. The model allowed to investigate, for the first time in patients with JIA, the functional response to joint impairment. The joint kinematics highlighted changes over time that were consistent with changes in the patient's clinical pattern and notably varied from patient to patient. The heterogeneous and patient-specific nature of the effects of JIA was confirmed by the absence of a correlation between a semi-quantitative MRI-based impairment score and a variety of investigated joint kinematics indexes. In conclusion, this study showed the feasibility of using MRI and morphological fitting to identify the tibiotalar and subtalar joint axes in a non-invasive patient-specific manner. The proposed methodology represents an innovative and reliable approach to the analysis of the ankle joint kinematics in pathological juvenile populations.

Greater magnitude tibiofemoral contact forces are associated with reduced prevalence of osteochondral pathologies 2-3 years following anterior cruciate ligament reconstruction.

PURPOSE: External loading of osteoarthritic and healthy knees correlates with current and future osteochondral tissue state. These relationships have not been examined following anterior cruciate ligament reconstruction. We hypothesised greater magnitude tibiofemoral contact forces were related to increased prevalence of osteochondral pathologies, and these relationships were exacerbated by concomitant meniscal injury. METHODS: This was a cross-sectional study of 100 individuals (29.7 ± 6.5 years, 78.1 ± 14.4 kg) examined 2-3 years following hamstring tendon anterior cruciate ligament reconstruction. Thirty-eight participants had concurrent meniscal pathology (30.6 ± 6.6 years, 83.3 ± 14.3 kg), which included treated and untreated meniscal injury, and 62 participants (29.8 ± 6.4 years, 74.9 ± 13.3 kg) were free of meniscal pathology. Magnetic resonance imaging of reconstructed knees was used to assess prevalence of tibiofemoral osteochondral pathologies (i.e., cartilage defects and bone marrow lesions). A calibrated electromyogram-driven neuromusculoskeletal model was used to predict medial and lateral tibiofemoral compartment contact forces from gait analysis data. Relationships between contact forces and osteochondral pathology prevalence were assessed using logistic regression models. RESULTS: In patients with reconstructed knees free from meniscal pathology, greater medial contact forces were related to reduced prevalence of medial cartilage defects (odds ratio (OR) = 0.7, Wald χ2(2) = 7.9, 95% confidence interval (CI) = 0.50-95, p = 0.02) and medial bone marrow lesions (OR = 0.8, Wald χ2(2) = 4.2, 95% CI = 0.7-0.99, p = 0.04). No significant relationships were found in lateral compartments. In reconstructed knees with concurrent meniscal pathology, no relationships were found between contact forces and osteochondral pathologies. CONCLUSIONS: In patients with reconstructed knees free from meniscal pathology, increased contact forces were associated with fewer cartilage defects and bone marrow lesions in medial, but not, lateral tibiofemoral compartments. No significant relationships were found between contact forces and osteochondral pathologies in reconstructed knees with meniscal pathology for any tibiofemoral compartment. Future studies should focus on determining longitudinal effects of contact forces and changes in osteochondral pathologies. LEVEL OF EVIDENCE: IV.