Longitudinal Associations of Clinical and Biochemical Head Injury Biomarkers With Head Impact Exposure in Adolescent Football Players.

Importance: Consequences of subconcussive head impacts have been recognized, yet most studies to date have included small samples from a single site, used a unimodal approach, and lacked repeated testing. Objective: To examine time-course changes in clinical (near point of convergence [NPC]) and brain-injury blood biomarkers (glial fibrillary acidic protein [GFAP], ubiquitin C-terminal hydrolase-L1 [UCH-L1], and neurofilament light [NF-L]) in adolescent football players and to test whether changes in the outcomes were associated with playing position, impact kinematics, and/or brain tissue strain. Design, Setting, and Participants: This multisite, prospective cohort study included male high school football players aged 13 to 18 years at 4 high schools in the Midwest during the 2021 high school football season (preseason [July] and August 2 to November 19). Exposure: A single football season. Main Outcomes and Measures: The main outcomes were NPC (a clinical oculomotor test) and serum levels of GFAP, UCH-L1, and NF-L. Participants' head impact exposure (frequency and peak linear and rotational accelerations) was tracked using instrumented mouthguards, and maximum principal strain was computed to reflect brain tissue strain. Players' neurological function was assessed at 5 time points (preseason, post-training camp, 2 in season, and postseason). Results: Ninety-nine male players contributed to the time-course analysis (mean [SD] age, 15.8 [1.1] years), but data from 6 players (6.1%) were excluded from the association analysis due to issues related to mouthguards. Thus, 93 players yielded 9498 head impacts in a season (mean [SD], 102 [113] impacts per player). There were time-course elevations in NPC and GFAP, UCH-L1, and NF-L levels. Compared with baseline, the NPC exhibited a significant elevation over time and peaked at postseason (2.21 cm; 95% CI, 1.80-2.63 cm; P < .001). Levels of GFAP and UCH-L1 increased by 25.6 pg/mL (95% CI, 17.6-33.6 pg/mL; P < .001) and 188.5 pg/mL (95% CI, 145.6-231.4 pg/mL; P < .001), respectively, later in the season. Levels of NF-L were elevated after the training camp (0.78 pg/mL; 95% CI, 0.14-1.41 pg/mL; P = .011) and midseason (0.55 pg/mL; 95% CI, 0.13-0.99 pg/mL; P = .006) but normalized by the end of the season. Changes in UCH-L1 levels were associated with maximum principal strain later in the season (0.052 pg/mL; 95% CI, 0.015-0.088 pg/mL; P = .007) and postseason (0.069 pg/mL; 95% CI, 0.031-0.106 pg/mL; P < .001). Conclusions and Relevance: The study data suggest that adolescent football players exhibited impairments in oculomotor function and elevations in blood biomarker levels associated with astrocyte activation and neuronal injury throughout a season. Several years of follow-up are needed to examine the long-term effects of subconcussive head impacts in adolescent football players.

A sensor-enabled cloud-based computing platform for computational brain biomechanics.

BACKGROUND AND OBJECTIVES: Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion. METHODS: We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline. RESULTS: The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D. CONCLUSION: We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.

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.

Fatigue damage prediction in the annulus of cervical spine intervertebral discs using finite element analysis.

Neck pain is a major inhibitor affecting the performance of U.S. military personnel. Repetitive exposure to cyclic loading due to military activities over several years can lead to accumulation of fatigue damage in the cervical intervertebral disc annuli, leading to neck pain. We have developed a computational damage model based on continuum damage mechanics, to predict fatigue damage to cervical disc annuli over several years of exposure to military loading scenarios. By integrating this fatigue damage model with a finite element model of the cervical spine, we have overcome the underlying assumption of a uniform stress distribution in the annulus. The resulting element-wise damage prediction gives us insight into the location of damage initiation and pattern of fatigue damage progression in the cervical disc annulus.

A computational model of bidirectional axonal growth in micro-tissue engineered neuronal networks (micro-TENNs).

Micro-Tissue Engineered Neural Networks (Micro-TENNs) are living three-dimensional constructs designed to replicate the neuroanatomy of white matter pathways in the brain and are being developed as implantable micro-tissue for axon tract reconstruction, or as anatomically-relevant in vitro experimental platforms. Micro-TENNs are composed of discrete neuronal aggregates connected by bundles of long-projecting axonal tracts within miniature tubular hydrogels. In order to help design and optimize micro-TENN performance, we have created a new computational model including geometric and functional properties. The model is built upon the three-dimensional diffusion equation and incorporates large-scale uni- and bi-directional growth that simulates realistic neuron morphologies. The model captures unique features of 3D axonal tract development that are not apparent in planar outgrowth and may be insightful for how white matter pathways form during brain development. The processes of axonal outgrowth, branching, turning and aggregation/bundling from each neuron are described through functions built on concentration equations and growth time distributed across the growth segments. Once developed we conducted multiple parametric studies to explore the applicability of the method and conducted preliminary validation via comparisons to experimentally grown micro-TENNs for a range of growth conditions. Using this framework, the model can be applied to study micro-TENN growth processes and functional characteristics using spiking network or compartmental network modeling. This model may be applied to improve our understanding of axonal tract development and functionality, as well as to optimize the fabrication of implantable tissue engineered brain pathways for nervous system reconstruction and/or modulation.

Computational model of a synovial joint morphogenesis.

Joints enable the relative movement between the connected bones. The shape of the joint is important for the joint movements since they facilitate and smooth the relative displacement of the joint's parts. The process of how the joints obtain their final shape is yet not well understood. Former models have been developed in order to understand the joint morphogenesis leaning only on the mechanical environment; however, the obtained final anatomical shape does not match entirely with a realistic geometry. In this study, a computational model was developed with the aim of explaining how the morphogenesis of joints and shaping of ossification structures are achieved. For this model, both the mechanical and biochemical environments were considered. It was assumed that cartilage growth was controlled by cyclic hydrostatic stress and inhibited by octahedral shear stress. In addition, molecules such as PTHrP and Wnt promote chondrocyte proliferation and therefore cartilage growth. Moreover, the appearance of the primary and secondary ossification centers was also modeled, for which the osteogenic index and PTHrP-Ihh concentrations were taken into account. The obtained results from this model show a coherent final shape of an interphalangeal joint, which suggest that the mechanical and biochemical environments are crucial for the joint morphogenesis process.

A coupled reaction-diffusion-strain model predicts cranial vault formation in development and disease.

How cells utilize instructions provided by genes and integrate mechanical forces generated by tissue growth to produce morphology is a fundamental question of biology. Dermal bones of the vertebrate cranial vault are formed through the direct differentiation of mesenchymal cells on the neural surface into osteoblasts through intramembranous ossification. Here we join a self-organizing Turing mechanism, computational biomechanics, and experimental data to produce a 3D representative model of the growing cerebral surface, cranial vault bones, and sutures. We show how changes in single parameters regulating signaling during osteoblast differentiation and bone formation may explain cranial vault shape variation in craniofacial disorders. A key result is that toggling a parameter in our model results in closure of a cranial vault suture, an event that occurred during evolution of the cranial vault and that occurs in craniofacial disorders. Our approach provides an initial and important step toward integrating biomechanics into the genotype phenotype map to explain the production of variation in head morphology by developmental mechanisms.

Biomechanics of Blast TBI With Time-Resolved Consecutive Primary, Secondary, and Tertiary Loads.

Blast-induced traumatic brain injury (bTBI) has become a signature casualty of recent military operations. In spite of significant clinical and preclinical TBI research, current understanding of injury mechanisms and short- and long-term outcomes is limited. Mathematical models of bTBI biomechanics may help in better understanding of injury mechanisms and in the development of improved neuroprotective strategies. Until present, bTBI has been analyzed as a single event of a blast pressure wave propagating through the brain. In many bTBI events, the loads on the body and the head are spatially and temporarily distributed, involving the primary intracranial pressure wave, followed by the head rotation and then by head impact on the ground. In such cases, the brain microstructures may experience time/space distributed (consecutive) damage and recovery events. The paper presents a novel multiscale simulation framework that couples the body/brain scale biomechanics with micro-scale mechanobiology to study the effects of micro-damage to neuro-axonal structures. Our results show that the micro-mechanical responses of neuro-axonal structures occur sequentially in time with "damage" and "relaxation" periods in different parts of the brain. A new integrated computational framework is described coupling the brain-scale biomechanics with micro-mechanical damage to axonal and synaptic structures.

Embedded Finite Elements for Modeling Axonal Injury.

The purpose of this paper is to propose and develop a large strain embedded finite element formulation that can be used to explicitly model axonal fiber bundle tractography from diffusion tensor imaging of the brain. Once incorporated, the fibers offer the capability to monitor tract-level strains that give insight into the biomechanics of brain injury. We show that one commercial software has a volume and mass redundancy issue when including embedded axonal fiber and that a newly developed algorithm is able to correct this discrepancy. We provide a validation analysis for stress and energy to demonstrate the method.

The Multi-Axial Failure Response of Porcine Trabecular Skull Bone Estimated Using Microstructural Simulations.

The development of a multi-axial failure criterion for trabecular skull bone has many clinical and biological implications. This failure criterion would allow for modeling of bone under daily loading scenarios that typically are multi-axial in nature. Some yield criteria have been developed to evaluate the failure of trabecular bone, but there is a little consensus among them. To help gain deeper understanding of multi-axial failure response of trabecular skull bone, we developed 30 microstructural finite element models of porous porcine skull bone and subjected them to multi-axial displacement loading simulations that spanned three-dimensional (3D) stress and strain space. High-resolution microcomputed tomography (microCT) scans of porcine trabecular bone were obtained and used to develop the meshes used for finite element simulations. In total, 376 unique multi-axial loading cases were simulated for each of the 30 microstructure models. Then, results from the total of 11,280 simulations (approximately 135,360 central processing unit-hours) were used to develop a mathematical expression, which describes the average three-dimensional yield surface in strain space. Our results indicate that the yield strain of porcine trabecular bone under multi-axial loading is nearly isotropic and despite a spread of yielding points between the 30 different microstructures, no significant relationship between the yield strain and bone volume fraction is observed. The proposed yield equation has simple format and it can be implemented into a macroscopic model for the prediction of failure of whole bones.

Assessing functional connectivity across 3D tissue engineered axonal tracts using calcium fluorescence imaging.

OBJECTIVE: Micro-tissue engineered neural networks (micro-TENNs) are anatomically-inspired constructs designed to structurally and functionally emulate white matter pathways in the brain. These 3D neural networks feature long axonal tracts spanning discrete neuronal populations contained within a tubular hydrogel, and are being developed to reconstruct damaged axonal pathways in the brain as well as to serve as physiologically-relevant in vitro experimental platforms. The goal of the current study was to characterize the functional properties of these neuronal and axonal networks. APPROACH: Bidirectional micro-TENNs were transduced to express genetically-encoded calcium indicators, and spontaneous fluorescence activity was recorded using real-time microscopy at 20 Hz from specific regions-of-interest in the neuronal populations. Network activity patterns and functional connectivity across the axonal tracts were then assessed using various techniques from statistics and information theory including Pearson cross-correlation, phase synchronization matrices, power spectral analysis, directed transfer function, and transfer entropy. MAIN RESULTS: Pearson cross-correlation, phase synchronization matrices, and power spectral analysis revealed high values of correlation and synchronicity between the spatially segregated neuronal clusters connected by axonal tracts. Specifically, phase synchronization revealed high synchronicity of >0.8 between micro-TENN regions of interest. Normalized directed transfer function and transfer entropy matrices suggested robust information flow between the neuronal populations. Time varying power spectrum analysis revealed the strength of information propagation at various frequencies. Signal power strength was visible at elevated peak levels for dominant delta (1-4 Hz) and theta (4-8 Hz) frequency bands and progressively weakened at higher frequencies. These signal power strength results closely matched normalized directed transfer function analysis where near synchronous information flow was detected between frequencies of 2-5 Hz. SIGNIFICANCE: To our knowledge, this is the first report using directed transfer function and transfer entropy methods based on fluorescent calcium activity to estimate functional connectivity of distinct neuronal populations via long-projecting, 3D axonal tracts in vitro. These functional data will further improve the design and optimization of implantable neural networks that could ultimately be deployed to reconstruct the nervous system to treat neurological disease and injury.

Do blast induced skull flexures result in axonal deformation?

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.

Engineered Axonal Tracts as "Living Electrodes" for Synaptic-Based Modulation of Neural Circuitry.

Brain-computer interface and neuromodulation strategies relying on penetrating non-organic electrodes/optrodes are limited by an inflammatory foreign body response that ultimately diminishes performance. A novel "biohybrid" strategy is advanced, whereby living neurons, biomaterials, and microelectrode/optical technology are used together to provide a biologically-based vehicle to probe and modulate nervous-system activity. Microtissue engineering techniques are employed to create axon-based "living electrodes", which are columnar microstructures comprised of neuronal population(s) projecting long axonal tracts within the lumen of a hydrogel designed to chaperone delivery into the brain. Upon microinjection, the axonal segment penetrates to prescribed depth for synaptic integration with local host neurons, with the perikaryal segment remaining externalized below conforming electrical-optical arrays. In this paradigm, only the biological component ultimately remains in the brain, potentially attenuating a chronic foreign-body response. Axon-based living electrodes are constructed using multiple neuronal subtypes, each with differential capacity to stimulate, inhibit, and/or modulate neural circuitry based on specificity uniquely afforded by synaptic integration, yet ultimately computer controlled by optical/electrical components on the brain surface. Current efforts are assessing the efficacy of this biohybrid interface for targeted, synaptic-based neuromodulation, and the specificity, spatial density and long-term fidelity versus conventional microelectronic or optical substrates alone.

A COMPUTATIONAL ANALYSIS OF BONE FORMATION IN THE CRANIAL VAULT USING A COUPLED REACTION-DIFFUSION-STRAIN MODEL.

Bones of the murine cranial vault are formed by differentiation of mesenchymal cells into osteoblasts, a process that is primarily understood to be controlled by a cascade of reactions between extracellular molecules and cells. We assume that the process can be modeled using Turing's reaction-diffusion equations, a mathematical model describing the pattern formation controlled by two interacting molecules (activator and inhibitor). In addition to the processes modeled by reaction-diffusion equations, we hypothesize that mechanical stimuli of the cells due to growth of the underlying brain contribute significantly to the process of cell differentiation in cranial vault development. Structural analysis of the surface of the brain was conducted to explore the effects of the mechanical strain on bone formation. We propose a mechanobiological model for the formation of cranial vault bones by coupling the reaction-diffusion model with structural mechanics. The mathematical formulation was solved using the finite volume method. The computational domain and model parameters are determined using a large collection of experimental data that provide precise three dimensional (3D) measures of murine cranial geometry and cranial vault bone formation for specific embryonic time points. The results of this study suggest that mechanical strain contributes information to specific aspects of bone formation. Our mechanobiological model predicts some key features of cranial vault bone formation that were verified by experimental observations including the relative location of ossification centers of individual vault bones, the pattern of cranial vault bone growth over time, and the position of cranial vault sutures.

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.

Modeling the mechanics of axonal fiber tracts using the embedded finite element method.

A subject-specific human head finite element model with embedded axonal fiber tractography obtained from diffusion tensor imaging was developed. The axonal fiber tractography finite element model was coupled with the volumetric elements in the head model using the embedded element method. This technique enables the calculation of axonal strains and real-time tracking of the mechanical response of the axonal fiber tracts. The coupled model was then verified using pressure and relative displacement-based (between skull and brain) experimental studies and was employed to analyze a head impact, demonstrating the applicability of this method in studying axonal injury. Following this, a comparison study of different injury criteria was performed. This model was used to determine the influence of impact direction on the extent of the axonal injury. The results suggested that the lateral impact loading is more dangerous compared to loading in the sagittal plane, a finding in agreement with previous studies. Through this analysis, we demonstrated the viability of the embedded element method as an alternative numerical approach for studying axonal injury in patient-specific human head models.

A computational analysis of bone formation in the cranial vault in the mouse.

Bones of the cranial vault are formed by the differentiation of mesenchymal cells into osteoblasts on a surface that surrounds the brain, eventually forming mineralized bone. Signaling pathways causative for cell differentiation include the actions of extracellular proteins driven by information from genes. We assume that the interaction of cells and extracellular molecules, which are associated with cell differentiation, can be modeled using Turing's reaction-diffusion model, a mathematical model for pattern formation controlled by two interacting molecules (activator and inhibitor). In this study, we hypothesize that regions of high concentration of an activator develop into primary centers of ossification, the earliest sites of cranial vault bone. In addition to the Turing model, we use another diffusion equation to model a morphogen (potentially the same as the morphogen associated with formation of ossification centers) associated with bone growth. These mathematical models were solved using the finite volume method. The computational domain and model parameters are determined using a large collection of experimental data showing skull bone formation in mouse at different embryonic days in mice carrying disease causing mutations and their unaffected littermates. The results show that the relative locations of the five ossification centers that form in our model occur at the same position as those identified in experimental data. As bone grows from these ossification centers, sutures form between the bones.

A MULTISCALE COMPUTATIONAL MODEL FOR THE GROWTH OF THE CRANIAL VAULT IN CRANIOSYNOSTOSIS.

Craniosynostosis is a condition defined by premature closure of cranial vault sutures, which is associated with abnormalities of the brain and skull. Many causal relationships between discovered mutations and premature suture closure have been proposed but an understanding of the precise mechanisms remains elusive. This article describes a computational framework of biological processes underlying cranial growth that will enable a hypothesis driven investigation of craniosynostosis phenotypes using reaction-diffusion-advection methods and the finite element method. Primary centers of ossification in cranial vault are found using activator-substrate model that represents the behavior of key molecules for bone formation. Biomechanical effects due to the interaction between growing bone and soft tissue is investigated to elucidate the mechanism of growth of cranial vault.

Combining the finite element method with structural connectome-based analysis for modeling neurotrauma: connectome neurotrauma mechanics.

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the "damaged" network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times (t < 24 hrs) network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight.