Contribution of Soft Tissue Passive Forces in Thumb Carpometacarpal Joint Distraction.

Thumb carpometacarpal joint space changes when the surrounding soft tissues including the capsule, ligaments, and tendons are stretched or pulled away. When at rest, joint forces originate from passive contraction of muscles and the involvement of joint capsule and ligaments. Previous biomechanical models of hand and finger joints have mostly focused on the assessment of joint properties when muscles were active. This study aims to present an experimental-numerical biomechanical model of thumb carpometacarpal joint to explore the contribution of tendons, ligaments, and other soft tissues in the passive forces during distraction. Five fresh cadaveric specimens were tested using a distractor device to measure the applied forces upon gradual distraction of the intact joint. The subsequent step involved inserting a minuscule sensor into the joint capsule through a small incision, while preserving the integrity of tendons and ligaments, in order to accurately measure the fundamental intra-articular forces. A numerical model was also used to calculate the passive forces of tendons and ligaments. Before the separation of bones, the forces exerted by tendons and ligaments were relatively small compared to the capsule force, which accounted for approximately 92% of the total applied force. Contribution of tendons and ligaments, however, increased by further distraction. The passive force contribution by tendons at 2-mm distraction was determined less than 11%, whereas it reached up to 74% for the ligaments. The present study demonstrated that the ligament-capsule complex plays significant contribution in passive forces of thumb carpometacarpal joint during distraction.

Bidirectional hyperelastic characterization of brain white matter tissue.

Biomechanical study of brain injuries originated from mechanical damages to white matter tissue requires detailed information on mechanical characteristics of its main components, the axonal fibers and extracellular matrix, which is very limited due to practical difficulties of direct measurement. In this paper, a new theoretical framework was established based on microstructural modeling of brain white matter tissue as a soft composite for bidirectional hyperelastic characterization of its main components. First the tissue was modeled as an Ogden hyperelastic material, and its principal Cauchy stresses were formulated in the axonal and transverse directions under uniaxial and equibiaxial tension using the theory of homogenization. Upon fitting these formulae to the corresponding experimental test data, direction-dependent hyperelastic constants of the tissue were obtained. These directional properties then were used to estimate the strain energy stored in the homogenized model under each loading scenario. A new microstructural composite model of the tissue was also established using principles of composites micromechanics, in which the axonal fibers and surrounding matrix are modeled as different Ogden hyperelastic materials with unknown constants. Upon balancing the strain energies stored in the homogenized and composite models under different loading scenarios, fully coupled nonlinear equations as functions of unknown hyperelastic constants were derived, and their optimum solutions were found in a multi-parametric multi-objective optimization procedure using the response surface methodology. Finally, these solutions were implemented, in a bottom-up approach, into a micromechanical finite element model to reproduce the tissue responses under the same loadings and predict the tissue responses under unseen non-equibiaxial loadings. Results demonstrated a very good agreement between the model predictions and experimental results in both directions under different loadings. Moreover, the axonal fibers with hyperelastic characteristics stiffer than the extracellular matrix were shown to play the dominant role in directional reinforcement of the tissue.

A new open-access platform for measuring and sharing mTBI data.

Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.

Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts.

Because of the rigid coupling between the upper dentition and the skull, instrumented mouthguards have been shown to be a viable way of measuring head impact kinematics for assisting in understanding the underlying biomechanics of concussions. This has led various companies and institutions to further develop instrumented mouthguards. However, their use as a research tool for understanding concussive impacts makes quantification of their accuracy critical, especially given the conflicting results from various recent studies. Here we present a study that uses a pneumatic impactor to deliver impacts characteristic to football to a Hybrid III headform, in order to validate and compare five of the most commonly used instrumented mouthguards. We found that all tested mouthguards gave accurate measurements for the peak angular acceleration, the peak angular velocity, brain injury criteria values (mean average errors < 13, 8, 13%, respectively), and the mouthguards with long enough sampling time windows are suitable for a convolutional neural network-based brain model to calculate the brain strain (mean average errors < 9%). Finally, we found that the accuracy of the measurement varies with the impact locations yet is not sensitive to the impact velocity for the most part.

A Vibrational Technique for In Vitro Intraoperative Prosthesis Fixation Monitoring.

OBJECTIVE: In this paper, a new vibrational modal analysis technique was developed for intraoperative cementless prosthesis fixation evaluation upon hammering. METHODS: An artificial bone (Sawbones)-prosthesis system was excited by sweeping of a sine signal over a wide frequency range. The exponential sine sweep technique was implemented to the response signal in order to determine the linear impulse response. Recursive Fourier transform enhancement (RFTE) technique was applied to the linear impulse response signal in order to enhance the frequency spectrum with sharp and distinguishable peak values indicating distinct high natural frequencies of the system (ranging from 15 kHz to 90 kHz). The experiment was repeated with 5 Sawbones-prosthesis samples. Upon successive hammering during the prosthesis insertion, variation of each natural frequency was traced. RESULTS: Compared to classical Fast Fourier Transform, RFTE provided a better tracing and enhancement of frequency components during insertion. Three different types of frequency evolving trends (monotonically increasing, insensitive, and plateau-like) were observed for all samples, as confirmed by a new finite element simulation of the prosthesis dynamic insertion. Two main mechanical phenomena (i.e., geometrical compaction and compressive stress) were shown to govern these trends in opposite ways. Follow-up of the plateau-like trend upon hammering showed that the frequency shift is a good indicator of fixation. CONCLUSION: Alongside the individual follow-up of frequency shifts, combinatorial frequency analysis provides new objective information on the mechanical stability of Sawbone-prosthesis fixation. SIGNIFICANCE: The proposed vibrational technique based on RTFE can provide the surgeon with a new assistive diagnostic technique during the surgery by indicating when the bone-prosthesis fixation is acceptable, and beyond of which further hammering should be done cautiously to avoid bone fracture.

Nonlinear mechanics of soft composites: hyperelastic characterization of white matter tissue components.

This paper presents a bi-directional closed-form analytical solution, in the framework of nonlinear soft composites mechanics, for top-down hyperelastic characterization of brain white matter tissue components, based on the directional homogenized responses of the tissue in the axial and transverse directions. The white matter is considered as a transversely isotropic neo-Hookean composite made of unidirectional distribution of axonal fibers within the extracellular matrix. First, two homogenization formulations are derived for the homogenized axial and transverse shear moduli of the tissue, based on definition of the strain energy density function. Next, the rule of mixtures and Hashin-Shtrikman theories are used to derive two coupled nonlinear equations which correlates the tissue shear moduli to these of its components. Closed-form solutions for shear moduli of the components are then obtained by solving these equations simultaneously. In order to validate the hyperelastic characteristics of components obtained in previous step, they are used in a bottom-up approach in a micromechanical model of the tissue with the aim of predicting the directional homogenized responses of the tissue. Comparison of model predictions with the experimental test results reported for corona radiata and corpus callosum white matter structures reveals very good agreements with the experimental results in both directions. The model predictions are also in good agreement with the analytical solution obtained by the iterated homogenization technique. Results indicate that axonal fibers are almost ten times stiffer than the extracellular matrix under large deformations.

A three-dimensional micromechanical model of brain white matter with histology-informed probabilistic distribution of axonal fibers.

This paper presents a three-dimensional micromechanical model of brain white matter tissue as a transversely isotropic soft composite described by the generalized Ogden hyperelastic model. The embedded element technique, with corrected stiffness redundancy in large deformations, was used for the embedment of a histology-informed probabilistic distribution of the axonal fibers in the extracellular matrix. The model was linked to a multi-objective, multi-parametric optimization algorithm, using the response surface methodology, for characterization of material properties of the axonal fibers and extracellular matrix in an inverse finite element analysis. The optimum hyperelastic characteristics of the tissue constituents, obtained based on the axonal and transverse direction test results of the corona radiata tissue samples, indicated that the axonal fibers were almost thirteen times stiffer than the extracellular matrix under large deformations. Simulation of the same tissue under a different loading condition, as well as that of another white matter tissue, i.e., the corpus callosum, in the axonal and transverse directions, using the optimized hyperelastic characteristics revealed tissue responses very close to those of the experiments. The results of the model at the sub-tissue level indicated that the stress concentrations were considerably large around the small axons, which might contribute into the brain injury.

Micromechanics of brain white matter tissue: A fiber-reinforced hyperelastic model using embedded element technique.

A transverse-plane hyperelastic micromechanical model of brain white matter tissue was developed using the embedded element technique (EET). The model consisted of a histology-informed probabilistic distribution of axonal fibers embedded within an extracellular matrix, both described using the generalized Ogden hyperelastic material model. A correcting method, based on the strain energy density function, was formulated to resolve the stiffness redundancy problem of the EET in large deformation regime. The model was then used to predict the homogenized tissue behavior and the associated localized responses of the axonal fibers under quasi-static, transverse, large deformations. Results indicated that with a sufficiently large representative volume element (RVE) and fine mesh, the statistically randomized microstructure implemented in the RVE exhibits directional independency in transverse plane, and the model predictions for the overall and local tissue responses, characterized by the normalized strain energy density and Cauchy and von Mises stresses, are independent from the modeling parameters. Comparison of the responses of the probabilistic model with that of a simple uniform RVE revealed that only the first one is capable of representing the localized behavior of the tissue constituents. The validity test of the model predictions for the corona radiata against experimental data from the literature indicated a very close agreement. In comparison with the conventional direct meshing method, the model provided almost the same results after correcting the stiffness redundancy, however, with much less computational cost and facilitated geometrical modeling, meshing, and boundary conditions imposing. It was concluded that the EET can be used effectively for detailed probabilistic micromechanical modeling of the white matter in order to provide more accurate predictions for the axonal responses, which are of great importance when simulating the brain trauma or tumor growth.