Determining skeletal muscle architecture with Laplacian simulations: a comparison with diffusion tensor imaging.

Determination of skeletal muscle architecture is important for accurately modeling muscle behavior. Current methods for 3D muscle architecture determination can be costly and time-consuming, making them prohibitive for clinical or modeling applications. Computational approaches such as Laplacian flow simulations can estimate muscle fascicle orientation based on muscle shape and aponeurosis location. The accuracy of this approach is unknown, however, since it has not been validated against other standards for muscle architecture determination. In this study, muscle architectures from the Laplacian approach were compared to those determined from diffusion tensor imaging in eight adult medial gastrocnemius muscles. The datasets were subdivided into training and validation sets, and computational fluid dynamics software was used to conduct Laplacian simulations. In training sets, inputs of muscle geometry, aponeurosis location, and geometric flow guides resulted in good agreement between methods. Application of the method to validation sets showed no significant differences in pennation angle (mean difference [Formula: see text] or fascicle length (mean difference 0.9 mm). Laplacian simulation was thus effective at predicting gastrocnemius muscle architectures in healthy volunteers using imaging-derived muscle shape and aponeurosis locations. This method may serve as a tool for determining muscle architecture in silico and as a complement to other approaches.

A 3D model of the Achilles tendon to determine the mechanisms underlying nonuniform tendon displacements.

The Achilles is the thickest tendon in the body and is the primary elastic energy-storing component during running. The form and function of the human Achilles is complex: twisted structure, intratendinous interactions, and differential motor control from the triceps surae muscles make Achilles behavior difficult to intuit. Recent in vivo imaging of the Achilles has revealed nonuniform displacement patterns that are not fully understood and may result from complex architecture and musculotendon interactions. In order to understand which features of the Achilles tendon give rise to the nonuniform deformations observed in vivo, we used computational modeling to predict the mechanical contributions from different features of the tendon. The aims of this study are to: (i) build a novel computational model of the Achilles tendon based on ultrashort echo time MRI, (ii) compare simulated displacements with published in vivo ultrasound measures of displacement, and (iii) use the model to elucidate the effects of tendon twisting, intratendon sliding, retrocalcaneal insertion, and differential muscle forces on tendon deformation. Intratendon sliding and differential muscle forces were found to be the largest factors contributing to displacement nonuniformity between tendon regions. Elimination of intratendon sliding or muscle forces reduced displacement nonuniformity by 96% and 85%, respectively, while elimination of tendon twist and the retrocalcaneal insertion reduced displacement nonuniformity by only 35% and 3%. These results suggest that changes in the complex internal structure of the tendon alter the interaction between muscle forces and tendon behavior and therefore may have important implications on muscle function during movement.

Contributions of the Musculus Uvulae to Velopharyngeal Closure Quantified With a 3-Dimensional Multimuscle Computational Model.

The convexity of the dorsal surface of the velum is critical for normal velopharyngeal (VP) function and is largely attributed to the levator veli palatini (LVP) and musculus uvulae (MU). Studies have correlated a concave or flat nasal velar surface to symptoms of VP dysfunction including hypernasality and nasal air emission. In the context of surgical repair of cleft palates, the MU has been given relatively little attention in the literature compared with the larger LVP. A greater understanding of the mechanics of the MU will provide insight into understanding the influence of a dysmorphic MU, as seen in cleft palate, as it relates to VP function. The purpose of this study was to quantify the contributions of the MU to VP closure in a computational model. We created a novel 3-dimensional (3D) finite element model of the VP mechanism from magnetic resonance imaging data collected from an individual with healthy noncleft VP anatomy. The model components included the velum, posterior pharyngeal wall (PPW), LVP, and MU. Simulations were based on the muscle and soft tissue mechanical properties from the literature. We found that, similar to previous hypotheses, the MU acts as (i) a space-occupying structure and (ii) a velar extensor. As a space-occupying structure, the MU helps to nearly triple the midline VP contact length. As a velar extensor, the MU acting alone without the LVP decreases the VP distance 62%. Furthermore, activation of the MU decreases the LVP activation required for closure almost 3-fold, from 20% (without MU) to 8% (with MU). Our study suggests that any possible salvaging and anatomical reconstruction of viable MU tissue in a cleft patient may improve VP closure due to its mechanical function. In the absence or dysfunction of MU tissue, implantation of autologous or engineered tissues at the velar midline, as a possible substitute for the MU, may produce a geometric convexity more favorable to VP closure. In the future, more complex models will provide further insight into optimal surgical reconstruction of the VP musculature in normal and cleft palate populations.

Muscle Synergies Heavily Influence the Neural Control of Arm Endpoint Stiffness and Energy Consumption.

Much debate has arisen from research on muscle synergies with respect to both limb impedance control and energy consumption. Studies of limb impedance control in the context of reaching movements and postural tasks have produced divergent findings, and this study explores whether the use of synergies by the central nervous system (CNS) can resolve these findings and also provide insights on mechanisms of energy consumption. In this study, we phrase these debates at the conceptual level of interactions between neural degrees of freedom and tasks constraints. This allows us to examine the ability of experimentally-observed synergies--correlated muscle activations--to control both energy consumption and the stiffness component of limb endpoint impedance. In our nominal 6-muscle planar arm model, muscle synergies and the desired size, shape, and orientation of endpoint stiffness ellipses, are expressed as linear constraints that define the set of feasible muscle activation patterns. Quadratic programming allows us to predict whether and how energy consumption can be minimized throughout the workspace of the limb given those linear constraints. We show that the presence of synergies drastically decreases the ability of the CNS to vary the properties of the endpoint stiffness and can even preclude the ability to minimize energy. Furthermore, the capacity to minimize energy consumption--when available--can be greatly affected by arm posture. Our computational approach helps reconcile divergent findings and conclusions about task-specific regulation of endpoint stiffness and energy consumption in the context of synergies. But more generally, these results provide further evidence that the benefits and disadvantages of muscle synergies go hand-in-hand with the structure of feasible muscle activation patterns afforded by the mechanics of the limb and task constraints. These insights will help design experiments to elucidate the interplay between synergies and the mechanisms of learning, plasticity, versatility and pathology in neuromuscular systems.

A Computational Model Quantifies the Effect of Anatomical Variability on Velopharyngeal Function.

PURPOSE: This study predicted the effects of velopharyngeal (VP) anatomical parameters on VP function to provide a greater understanding of speech mechanics and aid in the treatment of speech disorders. METHOD: We created a computational model of the VP mechanism using dimensions obtained from magnetic resonance imaging measurements of 10 healthy adults. The model components included the levator veli palatini (LVP), the velum, and the posterior pharyngeal wall, and the simulations were based on material parameters from the literature. The outcome metrics were the VP closure force and LVP muscle activation required to achieve VP closure. RESULTS: Our average model compared favorably with experimental data from the literature. Simulations of 1,000 random anatomies reflected the large variability in closure forces observed experimentally. VP distance had the greatest effect on both outcome metrics when considering the observed anatomic variability. Other anatomical parameters were ranked by their predicted influences on the outcome metrics. CONCLUSIONS: Our results support the implication that interventions for VP dysfunction that decrease anterior to posterior VP portal distance, increase velar length, and/or increase LVP cross-sectional area may be very effective. Future modeling studies will help to further our understanding of speech mechanics and optimize treatment of speech disorders.

A computational model of velopharyngeal closure for simulating cleft palate repair.

The levator veli palatini (LVP) muscle has long been recognized as the muscle that contributes most to velopharyngeal (VP) closure and is therefore of principal importance for restoring normal speech in patients with a cleft palate. Different surgical reconstructive procedures can utilize varying degrees of LVP overlap, and this study developed a new finite-element model of VP closure designed to understand the biomechanical effects of LVP overlap. A three-dimensional finite-element model was created from adult anatomical dimensions and parameters taken from the literature. Velopharyngeal function was simulated and compared with experimental measurements of VP closure force from a previous study. Varying degrees of overlap and separation of the LVP were simulated, and the corresponding closure force was calculated. The computational model compares favorably with the experimental measurements of closure force from the literature. Furthermore, the model predicts that there is an optimal level of overlap that maximizes the potential for the LVP to generate closure force. The model predicts that achieving optimal overlap can increase closure force up to roughly 100% when compared with too little or too much overlap. The results of using this new model of VP closure suggest that optimizing LVP overlap may produce improved surgical outcomes due to the intrinsic properties of muscle. Future work will compare these model predictions with clinical observations and provide further insights into optimal cleft palate repair and other craniofacial surgeries.

Towards undistorted and noise-free speech in an MRI scanner: correlation subtraction followed by spectral noise gating.

Noise cancellation in an MRI environment is difficult due to the high noise levels that are in the spectral range of human speech. This paper describes a two-step method to cancel MRI noise that combines operations in both the time domain (correlation subtraction) and the frequency domain (spectral noise gating). The resulting filtered recording has a noise power suppression of over 100 dB, a significant improvement over previously described techniques on MRI noise cancellation. The distortion is lower and the noise suppression higher than using spectral noise gating in isolation. Implementation of this method will aid in detailed studies of speech in relation to vocal tract and velopharyngeal function.

A Novel Synthesis of Computational Approaches Enables Optimization of Grasp Quality of Tendon-Driven Hands.

We propose a complete methodology to find the full set of feasible grasp wrenches and the corresponding wrench-direction-independent grasp quality for a tendon-driven hand with arbitrary design parameters. Monte Carlo simulations on two representative designs combined with multiple linear regression identified the parameters with the greatest potential to increase this grasp metric. This synthesis of computational approaches now enables the systematic design, evaluation, and optimization of tendon-driven hands.