Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes

Development of optically controlled living electrodes with long-projecting axon tracts for a synaptic brain-machine interface.

For implantable neural interfaces, functional/clinical outcomes are challenged by limitations in specificity and stability of inorganic microelectrodes. A biological intermediary between microelectrical devices and the brain may improve specificity and longevity through (i) natural synaptic integration with deep neural circuitry, (ii) accessibility on the brain surface, and (iii) optogenetic manipulation for targeted, light-based readout/control. Accordingly, we have developed implantable living electrodes, living cortical neurons, and axonal tracts protected within soft hydrogel cylinders, for optobiological monitoring/modulation of brain activity. Here, we demonstrate fabrication, rapid axonal outgrowth, reproducible cytoarchitecture, and simultaneous optical stimulation and recording of these tissue engineered constructs in vitro. We also present their transplantation, survival, integration, and optical recording in rat cortex as an in vivo proof of concept for this neural interface paradigm. The creation and characterization of these functional, optically controllable living electrodes are critical steps in developing a new class of optobiological tools for neural interfacing

A new computational approach for modeling diffusion tractography in the brain

Computational models provide additional tools for studying the brain, however, many techniques are currently disconnected from each other. There is a need for new computational approaches that span the range of physics operating in the brain. In this review paper, we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre. The embedded element method is a mesh superposition technique used within finite element analysis. This method allows for the incorporation of axonal fiber tracts to be explicitly represented. Here, we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury. We explore the potential application of the embedded element method in areas of electrophysiology, neurodegeneration, neuropharmacology and mechanobiology. We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies

Do blast induced skull flexures result in axonal deformation?

<div><p>Subject-specific computer models (male and female) of the human head were used to investigate the possible axonal deformation resulting from the primary phase blast-induced skull flexures. The corresponding axonal tractography was explicitly incorporated into these finite element models using a recently developed technique based on the embedded finite element method. These models were subjected to extensive verification against experimental studies which examined their pressure and displacement response under a wide range of loading conditions. Once verified, a parametric study was developed to investigate the axonal deformation for a wide range of loading overpressures and directions as well as varying cerebrospinal fluid (CSF) material models. This study focuses on early times during a blast event, just as the shock transverses the skull (< 5 milliseconds). Corresponding boundary conditions were applied to eliminate the rotation effects and the resulting axonal deformation. A total of 138 simulations were developed– 128 simulations for studying the different loading scenarios and 10 simulations for studying the effects of CSF material model variance–leading to a total of 10,702 simulation core hours. Extreme strains and strain rates along each of the fiber tracts in each of these scenarios were documented and presented here. The results suggest that the blast-induced skull flexures result in strain rates as high as 150–378 s^-1. These high-strain rates of the axonal fiber tracts, caused by flexural displacement of the skull, could lead to a rate dependent micro-structural axonal damage, as pointed by other researchers.</p></div

Results from the parametric study conducted on the male head model.

<p>(i) Axonal deformation in 64 studies, plotted against different blast overpressure magnitudes–(a) Maximum axonal strain rates, (b) Maximum axonal strains. (ii) Axonal deformation in 64 studies plotted against different blast loading directions–(c) Maximum axonal strain rates, (d) Maximum axonal strains.</p

Bio-fidelity scale against CORA ratings.

<p>A CORA rating should be greater than 0.26 for a comparison to be acceptable.</p

kull thickness across a plane of cross-section for both male and female FE models.

<p><b>S</b> The variation in skull thickness could be the reason for different flexural behavior across gender.</p

CORA ratings for the different brain-skull relative displacement validation plots (impact loading–occipital and frontal).

<p>Models were subjected to impact loading conditions, same as that of the experimental study by Hardy et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190881#pone.0190881.ref035" target="_blank">35</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190881#pone.0190881.ref036" target="_blank">36</a>].</p

Different loading conditions were used to determine the effect of variation in loading (direction and magnitude) on the axonal response.

<p>(a) ConWep blast loading curves. The different loading magnitudes simulated here include 1500 kPa, 1200 kPa, 900 kPa, 600 kPa, 300 kPa, 200 kPa, 100 kPa, 50 kPa. These blast loads are simulated using the ConWep tool in ABAQUS. (b) Blast loading conditions in comparison to Bowen’s lung threshold curve. This plot shows that all the loading conditions opted here fall below the threshold—indicating that the injury will not result in the death of the subject. (c) Arrangement of detonation points around the head form. This arrangement allows us to study the effect of variation in loading direction on the resulting axonal response. (d) Table shows the different ConWep parameters (Overpressure, ConWep charge, Detonation Distance and Positive Phase Duration) for the corresponding loading values used in this paper.</p

Different CSF material models used in the past literature.

<p>These different models used include (i) linear elastic, (ii) non-linear hyper-elastic and hyper-viscoelastic, (iii) fluid. Since CSF forms the interface between skull and the brain, the variance in the CSF material description could affect the model predictions.</p