Abstract
Despite years of research, it is still unknown whether the interaction of explosion-induced blast waves with the head causes injury to the human brain. One way to fill this gap is to use animal models to establish “scaling laws” that project observed brain injuries in animals to humans. This requires laboratory experiments and high-fidelity mathematical models of the animal head to establish correlates between experimentally observed blast-induced brain injuries and model-predicted biomechanical responses. To this end, we performed laboratory experiments on Göttingen minipigs to develop and validate a three-dimensional (3-D) high-fidelity finite-element (FE) model of the minipig head. First, we performed laboratory experiments on Göttingen minipigs to obtain the geometry of the cerebral vasculature network and to characterize brain-tissue and vasculature material properties in response to high strain rates typical of blast exposures. Next, we used the detailed cerebral vasculature information and species-specific brain tissue and vasculature material properties to develop the 3-D high-fidelity FE model of the minipig head. Then, to validate the model predictions, we performed laboratory shock-tube experiments, where we exposed Göttingen minipigs to a blast overpressure of 210 kPa in a laboratory shock tube and compared brain pressures at two locations. We observed a good agreement between the model-predicted pressures and the experimental measurements, with differences in maximum pressure of less than 6%. Finally, to evaluate the influence of the cerebral vascular network on the biomechanical predictions, we performed simulations where we compared results of FE models with and without the vasculature. As expected, incorporation of the vasculature decreased brain strain but did not affect the predictions of brain pressure. However, we observed that inclusion of the cerebral vasculature in the model changed the strain distribution by as much as 100% in regions near the interface between the vasculature and the brain tissue, suggesting that the vasculature does not merely decrease the strain but causes drastic redistributions. This work will help establish correlates between observed brain injuries and predicted biomechanical responses in minipigs and facilitate the creation of scaling laws to infer potential injuries in the human brain due to exposure to blast waves.
Highlights
Due to the lack of clinical data to assess the effects of blunt and blast loads to the human head, the prevailing approach is to study such phenomena in animal models and use “scaling laws” to project observed injuries in the animal brain to the human brain (Zhu et al, 2013a; Jean et al, 2014; Wu et al, 2021)
Our team developed a 3-D high-fidelity FE model of a rat head that accounts for the cerebral vasculature and uses ratspecific material properties characteristic of the high strain rates observed in blast exposures to represent the response of brain tissues and the vasculature (Unnikrishnan et al, 2019)
We developed a high-fidelity 3-D FE model of a Göttingen minipig head to characterize the biomechanical responses in the brain tissue resulting from a blast exposure in a laboratory shock tube
Summary
Due to the lack of clinical data to assess the effects of blunt and blast loads to the human head, the prevailing approach is to study such phenomena in animal models and use “scaling laws” to project observed injuries in the animal brain to the human brain (Zhu et al, 2013a; Jean et al, 2014; Wu et al, 2021). Our simulations showed that incorporation of the vasculature reduces the peak strain in the rat brain by as much as 33% and that the use of rat-specific material properties, instead of using those of humans, leads to a three-fold increase in the predicted strain Such a high-fidelity model does not exist for pigs, and it is needed because before using scaling laws to infer brain injuries in humans, we should first validate them between two species, which requires exposing animals to blast waves to observe brain injuries and the ability to accurately predict the associated biomechanical responses
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