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.

The kinematics and kinetics of head impact due to a standing fall onto a hard surface are summarized. Head injury due to impact from falls represents a significant problem, especially for older individuals. When the head is left unprotected during a fall, the impact severity can be high enough to cause significant injury or even death. To ascertain the range of head impact parameters, the dynamic response was captured for the pedestrian version of the 5th percentile female and 50th percentile male Hybrid III anthropomorphic test dummies as they were dropped from a standing position with different initial postures. Five scenarios of falls were considered including backward falls with/without hip flexion, forward falls with/without knee flexion and lateral falls. The results show that the head impact parameters are dependent on the fall scenario. A wide range of impact parameters was observed in 107 trials. The 95% prediction interval for the peak translational acceleration, peak angular acceleration, peak force, impact translational velocity and peak angular velocity are 146-502 g, 8.8-43.3 krad/s(2), 3.9-24.5 kN, 2.02-7.41 m/s, and 12.9-70.3 rad/s, respectively.