Bayesian Calibration of Computational Knee Models to Estimate Subject-Specific Ligament Properties, Tibiofemoral Kinematics, and Anterior Cruciate Ligament Force With Uncertainty Quantification.

High-grade knee laxity is associated with early anterior cruciate ligament (ACL) graft failure, poor function, and compromised clinical outcome. Yet, the specific ligaments and ligament properties driving knee laxity remain poorly understood. We described a Bayesian calibration methodology for predicting unknown ligament properties in a computational knee model. Then, we applied the method to estimate unknown ligament properties with uncertainty bounds using tibiofemoral kinematics and ACL force measurements from two cadaver knees that spanned a range of laxities; these knees were tested using a robotic manipulator. The unknown ligament properties were from the Bayesian set of plausible ligament properties, as specified by their posterior distribution. Finally, we developed a calibrated predictor of tibiofemoral kinematics and ACL force with their own uncertainty bounds. The calibrated predictor was developed by first collecting the posterior draws of the kinematics and ACL force that are induced by the posterior draws of the ligament properties and model parameters. Bayesian calibration identified unique ligament slack lengths for the two knee models and produced ACL force and kinematic predictions that were closer to the corresponding in vitro measurement than those from a standard optimization technique. This Bayesian framework quantifies uncertainty in both ligament properties and model outputs; an important step towards developing subject-specific computational models to improve treatment for ACL injury.

Subcellular Remodeling in Filamin C Deficient Mouse Hearts Impairs Myocyte Tension Development during Progression of Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is a life-threatening form of heart disease that is typically characterized by progressive thinning of the ventricular walls, chamber dilation, and systolic dysfunction. Multiple mutations in the gene encoding filamin C (FLNC), an actin-binding cytoskeletal protein in cardiomyocytes, have been found in patients with DCM. However, the mechanisms that lead to contractile impairment and DCM in patients with FLNC variants are poorly understood. To determine how FLNC regulates systolic force transmission and DCM remodeling, we used an inducible, cardiac-specific FLNC-knockout (icKO) model to produce a rapid onset of DCM in adult mice. Loss of FLNC reduced systolic force development in single cardiomyocytes and isolated papillary muscles but did not affect twitch kinetics or calcium transients. Electron and immunofluorescence microscopy showed significant defects in Z-disk alignment in icKO mice and altered myofilament lattice geometry. Moreover, a loss of FLNC induces a softening myocyte cortex and structural adaptations at the subcellular level that contribute to disrupted longitudinal force production during contraction. Spatially explicit computational models showed that these structural defects could be explained by a loss of inter-myofibril elastic coupling at the Z-disk. Our work identifies FLNC as a key regulator of the multiscale ultrastructure of cardiomyocytes and therefore plays an important role in maintaining systolic mechanotransmission pathways, the dysfunction of which may be key in driving progressive DCM.

Femoral Component Malrotation Produces Quadriceps Weakness and Impaired Ambulatory Function following Total Knee Arthroplasty: Results of a Forward-Dynamic Computer Model.

Proper placement of the prosthetic components is believed to be an important factor in successful total knee arthroplasty (TKA). Implant positioning errors have been associated with postoperative pain, suboptimal function, and inferior patient-reported outcome measures. The purpose of this study was to investigate the biomechanical effects of femoral component malrotation on quadriceps function and normal ambulation. For the investigation, publicly available data were used to create a validated forward-dynamic, patient-specific computer model. The incorporated data included medical imaging, gait laboratory measurements, knee loading information, electromyographic data, strength testing, and information from the surgical procedure. The ideal femoral component rotation was set to the surgical transepicondylar axis and walking simulations were subsequently performed with increasing degrees of internal and external rotation of the femoral component. The muscle force outputs were then recorded for the quadriceps musculature as a whole, as well as for the individual constituent muscles. The quadriceps work requirements during walking were then calculated for the different rotational simulations. The highest forces generated by the quadriceps were seen during single-limb stance phase as increasing degrees of femoral internal rotation produced proportional increases in quadriceps force requirements. The individual muscles of the quadriceps displayed different sensitivities to the rotational variations introduced into the simulations with the vastus lateralis showing the greatest changes with rotational positioning. Increasing degrees of internal rotation of femoral component were also seen to demand increasing quadriceps work to support normal ambulation. In conclusion, internal malrotation of the femoral component during TKA produces a mechanically disadvantaged state which is characterized by greater required quadriceps forces (especially the vastus lateralis) and greater quadriceps work to support normal ambulation.

Electromyography-Driven Forward Dynamics Simulation to Estimate In Vivo Joint Contact Forces During Normal, Smooth, and Bouncy Gaits.

Computational models that predict in vivo joint loading and muscle forces can potentially enhance and augment our knowledge of both typical and pathological gaits. To adopt such models into clinical applications, studies validating modeling predictions are essential. This study created a full-body musculoskeletal model using data from the "Sixth Grand Challenge Competition to Predict in vivo Knee Loads." This model incorporates subject-specific geometries of the right leg in order to concurrently predict knee contact forces, ligament forces, muscle forces, and ground contact forces. The objectives of this paper are twofold: (1) to describe an electromyography (EMG)-driven modeling methodology to predict knee contact forces and (2) to validate model predictions by evaluating the model predictions against known values for a patient with an instrumented total knee replacement (TKR) for three distinctly different gait styles (normal, smooth, and bouncy gaits). The model integrates a subject-specific knee model onto a previously validated generic full-body musculoskeletal model. The combined model included six degrees-of-freedom (6DOF) patellofemoral and tibiofemoral joints, ligament forces, and deformable contact forces with viscous damping. The foot/shoe/floor interactions were modeled by incorporating shoe geometries to the feet. Contact between shoe segments and the floor surface was used to constrain the shoe segments. A novel EMG-driven feedforward with feedback trim motor control strategy was used to concurrently estimate muscle forces and knee contact forces from standard motion capture data collected on the individual subject. The predicted medial, lateral, and total tibiofemoral forces represented the overall measured magnitude and temporal patterns with good root-mean-squared errors (RMSEs) and Pearson's correlation (p2). The model accuracy was high: medial, lateral, and total tibiofemoral contact force RMSEs = 0.15, 0.14, 0.21 body weight (BW), and (0.92 < p2 < 0.96) for normal gait; RMSEs = 0.18 BW, 0.21 BW, 0.29 BW, and (0.81 < p2 < 0.93) for smooth gait; and RMSEs = 0.21 BW, 0.22 BW, 0.33 BW, and (0.86 < p2 < 0.95) for bouncy gait, respectively. Overall, the model captured the general shape, magnitude, and temporal patterns of the contact force profiles accurately. Potential applications of this proposed model include predictive biomechanics simulations, design of TKR components, soft tissue balancing, and surgical simulation.

Function of the Anterior Intermeniscal Ligament.

The function and importance of the anterior intermeniscal ligament (AIML) of the knee are not fully known. The purpose of this study was to evaluate the biomechanical and sensorimotor function of the AIML. Computational analysis was used to assess AIML and tibiomeniscofemoral biomechanics under combined translational and rotational loading applied during dynamic knee flexion-extension. Histologic and immunohistochemical examination was used to identify and characterize neural elements in the tissue. The computational models were created from anatomy and passive motion of two female subjects and histologic examinations were conducted on AIMLs retrieved from 10 fresh-frozen cadaveric knees. It was found that AIML strain increased with compressive knee loading and that external rotation of the tibia unloads the AIML, suppressing the relationship between AIML strain and compressive knee loads. Extensive neural elements were located throughout the AIML tissue and these elements were distributed across the three AIML anatomical types. The AIMLs have a beneficial influence on knee biomechanics with decreased meniscal load sharing with AIML loss. The AIML plays a significant biomechanical and neurologic role in the sensorimotor functions of the knee. The major role for the AIML may primarily involve its neurologic function.