The influence of equine limb conformation on the biomechanical responses of the hoof: An in vivo and finite element study

Hoof conformation plays a key role in equine locomotion. Toe-in conformation is an abnormal condition characterized by inward deviation of the limb from its frontal axis. Several studies have documented differences in hoof deformation and hoof kinematics in horses with toe-in and normal hoof conformations. However, the reason behind this has yet to be understood. The present study hypothesizes that a different center of pressure (COP) path underneath the hoof is the cause of different deformation patterns and hoof kinematics in toe-in hooves. In vivo measurements and finite element (FE) analysis were conducted to test the hypothesis. A normal and a toe-in limb were considered for in vivo strain measurements. Strains were measured at three different sites on the hoof wall, and the stride characteristics were investigated using video recording. The magnitude of the minimum principal strain measured at the medial aspect of the toe-in hoof was much lower relative to the normal hoof. Furthermore, the toe-in hoof had a different movement pattern (plaiting) compared to the normal hoof. In the second study, an entire hoof model was simulated from computed tomography (CT) scans of an equine left forelimb. The Neo-Hookean hyperelastic material model was used, and the hoof was under dynamic loading over a complete stride at the trot. Two different COP paths associated with normal and toe-in conformations were assigned to the model. The FE model produced the same in vivo minimum principal strain distributions and successfully showed the different kinematics of the toe-in and normal hooves.

Artificial intelligence data-driven 3D model for AIS

Scoliosis is a 3D deformation of the spinal column, characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS), is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. The selection of the most appropriate treatment options is based on the surgeon's experience. So, developing a clinically validated patient-specific model of the spine would aid surgeons in understanding AIS in early stages and propose an efficient method of treatment for the individual patient. This project steps include: Developing a clinically validated patient-specific Reduced Order Finite Element Model (ROFEM) of the spine, predicting AIS progression using data mining and proposing a method of treatment. First we implement FE synergistically with bio-mechanical information, image processing and data science techniques to improve predictive ability. Initial geometry of the spine will be extracted from the x-ray images from different planes and imported to FEM software to generate the spine model and perform analysis. A RO model is developed based on the detailed spinal FEM. Next, a neural network is used to predict the spinal curvature. The ability to predict the severity of AIS will have an immense impact on the treatment of AIS-affected children. Access to a predictive and patient-specific model will enable the physicians to have a better understanding of spinal curvature progression. Consequently, the physicians will be able to educate families, choose the most appropriate treatment option and asses for surgical intervention.

Effect of macro-calcification on the failure mechanics of intracranial aneurysmal wall tissue

BACKGROUND

Calcification was recently found to be present in the majority of cerebral aneurysms, though how calcification and the presence or absence of co-localized lipid pools affect failure properties is still unknown.

OBJECTIVE

The primary objective is to quantify the biomechanical effect of a macro-calcification with surrounding Near-Calcification Region (NCR) of varying mechanical properties on tissue failure behavior.

METHODS

We utilized a structurally informed finite element model to simulate pre-failure and failure behavior of a human cerebral tissue specimen modeled as a composite containing a macro-calcification and surrounding NCR, embedded in a fiber matrix composite. Data from multiple imaging modalities was combined to quantify the collagen organization and calcification geometry. An idealized parametric model utilizing the calibrated model was used to explore the impact of NCR properties on tissue failure.

RESULTS

Compared to tissue without calcification, peak stress was reduced by 82% and 49% for low modulus (representing lipid pool) and high modulus (simulating increase in calcification size) of the NCR, respectively. Failure process strongly depended on NCR properties with lipid pools blunting the onset of complete failure. When the NCR was calcified, the sample was able to sustain larger overall stress, however the failure process was abrupt with nearly simultaneous failure of the loaded fibers.

CONCLUSIONS

Failure of calcified vascular tissue is strongly influenced by the ultrastructure in the vicinity of the calcification. Computational modeling of failure in fibrous soft tissues can be used to understand how pathological changes impact the tissue failure process, with potentially important clinical implications.

The Quasi-uniform assumption for Spinal Cord Stimulation translational research

BACKGROUND

Quasi-uniform assumption is a general theory that postulates local electric field predicts neuronal activation. Computational current flow model of spinal cord stimulation (SCS) of humans and animal models inform how the quasi-uniform assumption can support scaling neuromodulation dose between humans and translational animal.

NEW METHOD

Here we developed finite element models of cat and rat SCS, and brain slice, alongside SCS models. Boundary conditions related to species specific electrode dimensions applied, and electric fields per unit current (mA) predicted.

RESULTS

Clinically and across animal, electric fields change abruptly over small distance compared to the neuronal morphology, such that each neuron is exposed to multiple electric fields. Per unit current, electric fields generally decrease with body mass, but not necessarily and proportionally across tissues. Peak electric field in dorsal column rat and cat were ∼17x and ∼1x of clinical values, for scaled electrodes and equal current. Within the spinal cord, the electric field for rat, cat, and human decreased to 50% of peak value caudo-rostrally (C5-C6) at 0.48 mm, 3.2 mm, and 8 mm, and mediolaterally at 0.14 mm, 2.3 mm, and 3.1 mm. Because these space constants are different, electric field across species cannot be matched without selecting a region of interest (ROI).

COMPARISON WITH EXISTING METHOD

This is the first computational model to support scaling neuromodulation dose between humans and translational animal.

CONCLUSIONS

Inter-species reproduction of the electric field profile across the entire surface of neuron populations is intractable. Approximating quasi-uniform electric field in a ROI is a rational step to translational scaling.

Mechanical properties of a nanoporous membrane used in implantable medical devices. Correlation between experimental characterization and 2D numerical simulation

Nanoporous membranes are used for the elaboration of implantable medical devices. In order to guaranty their integrity after implantation in a patient body, it is necessary to characterize the microstructure and the mechanical behavior of such membranes. They present randomly distributed pores around 1µm in diameter at the surface. X-ray nanotomography permits to get the geometry of the pores through the thickness with a reduction of the diameter in the core. A multiscale study is done to characterize the membranes: macroscopic tensile tests permit to get the behavior law of the non porous material and in situ tensile tests are carried on in a Scanning Electron Microscope in order to observe the evolution of pores and cracks during loading. A 2D Finite Element Model is also developed in parallel. The confrontation between experiments and numerical simulations permit to validate the accuracy of the model. The latter is then used to simulate several types of loadings considering various pore distributions and sizes.

Modeling sequence and quasi-uniform assumption in computational neurostimulation

Computational neurostimulation aims to develop mathematical constructs that link the application of neuromodulation with changes in behavior and cognition. This process is critical but daunting for technical challenges and scientific unknowns. The overarching goal of this review is to address how this complex task can be made tractable. We describe a framework of sequential modeling steps to achieve this: (1) current flow models, (2) cell polarization models, (3) network and information processing models, and (4) models of the neuroscientific correlates of behavior. Each step is explained with a specific emphasis on the assumptions underpinning underlying sequential implementation. We explain the further implementation of the quasi-uniform assumption to overcome technical limitations and unknowns. We specifically focus on examples in electrical stimulation, such as transcranial direct current stimulation. Our approach and conclusions are broadly applied to immediate and ongoing efforts to deploy computational neurostimulation.

Impact of brain tissue filtering on neurostimulation fields: a modeling study

Electrical neurostimulation techniques, such as deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS), are increasingly used in the neurosciences, e.g., for studying brain function, and for neurotherapeutics, e.g., for treating depression, epilepsy, and Parkinson's disease. The characterization of electrical properties of brain tissue has guided our fundamental understanding and application of these methods, from electrophysiologic theory to clinical dosing-metrics. Nonetheless, prior computational models have primarily relied on ex-vivo impedance measurements. We recorded the in-vivo impedances of brain tissues during neurosurgical procedures and used these results to construct MRI guided computational models of TMS and DBS neurostimulatory fields and conductance-based models of neurons exposed to stimulation. We demonstrated that tissues carry neurostimulation currents through frequency dependent resistive and capacitive properties not typically accounted for by past neurostimulation modeling work. We show that these fundamental brain tissue properties can have significant effects on the neurostimulatory-fields (capacitive and resistive current composition and spatial/temporal dynamics) and neural responses (stimulation threshold, ionic currents, and membrane dynamics). These findings highlight the importance of tissue impedance properties on neurostimulation and impact our understanding of the biological mechanisms and technological potential of neurostimulatory methods.