Integration of Interspecies Data for Developing Tissue-Level Brain Injury Risk Functions

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Wu, Taotao, Mechanical and Aerospace Engineering - School of Engineering and Applied Science, University of Virginia
Crandall, Jeff, Department of Mechanical and Aerospace Engineering, University of Virginia
Panzer, Matthew, Department of Mechanical and Aerospace Engineering, University of Virginia

Traumatic brain injuries (TBI) are a significant public health burden occurring in automotive crashes, accidents, sports, and in military training and combat. There is a significant interest in understanding the tolerance of the human brain to external mechanical loads with the ultimate objective of mitigation and prevention of TBI. Early TBI research focused on understanding the injury mechanisms in animals, and the latest research focus has been on collecting exposure data in humans that routinely experience head impacts to quantify injury risks. Both research approaches have major limitations when studied in isolation, but when integrated they may provide a complete picture on TBI mechanisms and risk. One of the biggest challenges to forming a more comprehensive understanding of TBI risk is the applicability of animal brain injury data to humans. Therefore, the objective of this dissertation was to integrate human and animal brain injury data to establish a unique brain injury dataset that will be used to develop tissue-level brain injury risk functions. Finite element (FE) simulations were used to bridge the interspecies gap between human and non-human primate (NHP) injury data, assuming the equivalence of tissue-level metrics across primates.

To achieve the goals, advanced multi-scale FE models of the human and NHP (macaque and baboon) brains were developed by explicitly incorporating mesoscopic anatomical details (axonal tracts) using a novel embedding method. Mechanical behaviors of the brain tissue were modeled with a hyper-viscoelastic constitutive model, calibrated with available multi-modal testing data of in vitro brain tissue and extensively validated for in situ and in vivo intracranial deformations under various loading conditions. The numerical methods, anatomical features (axonal tractography), and constitutive models in these FE models were harmonized to facilitate the study of tissue-level responses across models of different species.

Utilizing these computational tools, this dissertation presents two new methods to derive brain injury risk functions by integrating NHP and human brain injury data. First, a cross-species scaling method was formulated to correlate animal exposure data to humans, specifically to find the equivalent biomechanical impact conditions that result in similar tissue-level mechanical responses for different species. Recognizing the resonance of the brain deformation under rotational motion, a new brain injury scaling method was developed based on scaling the natural frequency of the brain. The results of this work indicate that previously described biomechanical scaling methods, often based on the relative mass of each species, were poor predictors of the equivalent biomechanical impact conditions between NHP and human. The physically-bounded frequency-based scaling method improved the accuracy of scaling the equivalent loading conditions and provided insight to account for the interspecies differences in brain physical morphology, anatomy and tissue properties.

Second, a methodology for integrating interspecies injury data to derive human brain injury risk functions was developed through the harmonized brain FE models of the human, macaque and baboon. The efficacy of the tissue-level injury metrics for predicting injury was evaluated computationally by simulation of an integrated dataset of sub-injurious human volunteer sled tests, laboratory reconstructed head impacts from professional football, and in vivo NHP tests. The current analysis lends some favor to Von Mises stress and maximum principal strain over other existing tissue-level metrics as good predictors of injury, while no evidence was shown that the global axonal strain was a better predictor of injury than the global principal strain. Associated injury risk functions for mild and severe TBI were proposed through integrated data. Efficacy of the proposed injury risk functions was first verified by an independent test dataset and eventually applied to automotive crash scenarios to ensure the proper usage of the risk functions.

The main contributions of this dissertation were the new methods for developing tissue-level brain injury risk functions using injury data of multiple species. The findings and the developed methods could be of critical importance in guiding the technical innovation of more effective safety countermeasures, thereby, reducing the incidences, consequences, and societal burden of TBI.

PHD (Doctor of Philosophy)
Traumatic Brain Injury, Finite Element Modeling, Brain Injury Tolerance, Cross-species Scaling
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