With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI). All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73–90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR_7.5) that represents the fraction of axonal fibers exceeding MASxSR of 7.5 s⁻¹. The thresholds compare favorably with tissue tolerances found in in–vitro/in–vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05–4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02–1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11–41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.

Finite element models (FEMs) are used increasingly in the traumatic brain injury (TBI) field to provide an estimation of tissue responses and predict the probability of sustaining TBI after a biomechanical event. However, FEM studies have mainly focused on predicting the absence/presence of TBI rather than estimating the location of injury. In this study, we created a multi-scale FEM of the pig brain with embedded axonal tracts to estimate the sites of acute (≤6 h) traumatic axonal injury (TAI) after rapid head rotation. We examined three finite element (FE)-derived metrics related to the axonal bundle deformation and three FE-derived metrics based on brain tissue deformation for prediction of acute TAI location. Rapid head rotations were performed in pigs, and TAI neuropathological maps were generated and colocalized to the FEM. The distributions of the FEM-derived brain/axonal deformations spatially correlate with the locations of acute TAI. For each of the six metric candidates, we examined a matrix of different injury thresholds (th _x ) and distance to actual TAI sites (d _s ) to maximize the average of two optimization criteria. Three axonal deformation-related TAI candidates predicted the sites of acute TAI within 2.5 mm, but no brain tissue metric succeeded. The optimal range of thresholds for maximum axonal strain, maximum axonal strain rate, and maximum product of axonal strain and strain rate were 0.08–0.14, 40–90, and 2.0–7.5 s ⁻¹ , respectively. The upper bounds of these thresholds resulted in higher true-positive prediction rate. In summary, this study confirmed the hypothesis that the large axonal-bundle deformations occur on/close to the areas that sustained TAI.

Head rotational kinematics and tissue deformation metrics obtained from finite element models (FEM) have the potential to be used as traumatic axonal injury (TAI) assessment criteria and headgear evaluation standards. These metrics have been used to predict the likelihood of TAI occurrence; however, their ability in the assessment of the extent of TAI has not been explored. In this study, a pig model of TAI was used to examine a wide range of head loading conditions in two directions. The extent of TAI was quantified through histopathology and correlated to the FEM-derived tissue deformations and the head rotational kinematics. Peak angular acceleration and maximum strain rate of axonal fiber and brain tissue showed relatively good correlation to the volume of axonal injury, with similar correlation trends for both directions separately or combined. These rotational kinematics and tissue deformations can estimate the extent of acute TAI. The relationships between the head kinematics and the tissue strain, strain rate, and strain times strain rate were determined over the experimental range examined herein, and beyond that through parametric simulations. These relationships demonstrate that peak angular velocity and acceleration affect the underlying tissue deformations and the knowledge of both help to predict TAI risk. These relationships were combined with the injury thresholds, extracted from the TAI risk curves, and the kinematic-based risk curves representing overall axonal and brain tissue strain and strain rate were determined for predicting TAI. After scaling to humans, these curves can be used for real-time TAI assessment.

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. There is an increasing interest in both clinical and pre-clinical studies to discover biomarkers to accurately diagnose traumatic brain injury (TBI), predict its outcomes, and monitor its progression especially in the developing brain. In humans, the heterogeneity of TBI in terms of clinical presentation, injury causation, and mechanism has contributed to the many challenges associated with finding unifying diagnosis, treatment, and management practices. In addition, findings from adult human research may have little application to pediatric TBI, as age and maturation levels affect the injury biomechanics and neurophysiological consequences of injury. Animal models of TBI are vital to address the variability and heterogeneity of TBI seen in human by isolating the causation and mechanism of injury in reproducible manner. However, a gap between the pre-clinical findings and clinical applications remains in TBI research today. To take a step toward bridging this gap, we reviewed several potential TBI tools such as biofluid biomarkers, electroencephalography (EEG), actigraphy, eye responses, and balance that have been explored in both clinical and pre-clinical studies and have shown potential diagnostic, prognostic, or monitoring utility for TBI. Each of these tools measures specific deficits following TBI, is easily accessible, non/minimally invasive, and is potentially highly translatable between animals and human outcomes because they involve effort-independent and non-verbal tasks. Especially conspicuous is the fact that these biomarkers and techniques can be tailored for infants and toddlers. However, translation of preclinical outcomes to clinical applications of these tools necessitates addressing several challenges. Among the challenges are the heterogeneity of clinical TBI, age dependency of some of the biomarkers, different brain structure, life span, and possible variation between temporal profiles of biomarkers in human and animals. Conducting parallel clinical and pre-clinical research, in addition to the integration of findings across species from several pre-clinical models to generate a spectrum of TBI mechanisms and severities is a path toward overcoming some of these challenges. This effort is possible through large scale collaborative research and data sharing across multiple centers. In addition, TBI causes dynamic deficits in multiple domains, and thus, a panel of biomarkers combining these measures to consider different deficits is more promising than a single biomarker for TBI. In this review, each of these tools are presented along with the clinical and pre-clinical findings, advantages, challenges and prospects of translating the pre-clinical knowledge into the human clinical setting.

Skull fractures are common injuries in young children, typically caused by accidental falls and child abuse. The paucity of detailed biomechanical data from real-world trauma in children has hampered development of biomechanical thresholds for skull fracture in infants. The objectives of this study were to identify biomechanical metrics to predict skull fracture, determine threshold values associated with fracture, and develop skull fracture risk curves for low-height falls in infants. To achieve these objectives, we utilized an integrated approach consisting of case evaluation, anthropomorphic reconstruction, and finite element simulation. Four biomechanical candidates for predicting skull fracture were identified (first principal stress, first principal strain, shear stress, and von Mises stress) and evaluated against well-witnessed falls in infants (0–6 months). Among the predictor candidates, first principal stress and strain correlated best with the occurrence of parietal skull fracture. The principal stress and strain thresholds associated with 50 and 95% probability of parietal skull fracture were 25.229 and 36.015 MPa and 0.0464 and 0.0699, respectively. Risk curves using these predictors determined that infant falls from 0.3 m had a low probability (0–54%) to result in parietal skull fracture, particularly with carpet impact (0–1%). Head-first falls from 0.9 m had a high probability of fracture (86–100%) for concrete impact and a moderate probability (34–81%) for carpet impact. Probabilities of fracture in 0.6 m falls were dependent on impact surface. Occipital impacts from 0.9 m onto the concrete also had the potential (27–90% probability) to generate parietal skull fracture. These data represent a multi-faceted biomechanical assessment of infant skull fracture risk and can assist in the differential diagnosis for head trauma in children.

Traumatic brain injury (TBI) can cause biochemical and metabolomic alterations in the brain tissue and serum. These alterations can be used for diagnosis and prognosis of TBI. Here, the serum concentrations of seventeen amino acids (AA) were studied for their potential utility as biomarkers of TBI. Twenty-five female, 4-week-old piglets received diffuse (n = 13) or focal (n = 12) TBI. Blood samples were obtained both pre-injury and at either 24-h or 4-days post-TBI. To find a robust panel of biomarkers, the results of focal and diffuse TBIs were combined and multivariate logistic regression analysis, coupled with the best subset selection technique and repeated k-fold cross-validation method, was used to perform a thorough search of all possible subsets of AAs. The combination of serum glycine, taurine, and ornithine was optimal for TBI diagnosis, with 80% sensitivity and 86% overall prediction rate, and showed excellent TBI diagnostic performance, with 100% sensitivity and 78% overall prediction rate, on a separate validation dataset including four uninjured and five injured animals. We found that combinations of biomarkers outperformed any single biomarker. We propose this 3-AA serum biomarker panel to diagnose mild-to-moderate focal/diffuse TBI. The systematic approaches implemented herein can be used for combining parameters from various TBI assessments to develop/evaluate optimal multi-factorial diagnostic/prognostic TBI metrics.