Compressive properties and constitutive modeling of different regions of 8-week-old pediatric porcine brain under large strain and wide strain rates

摘要

Porcine head is often used as a human surrogate in traumatic head injury research. Extensive research on mechanical properties of adult human/porcine brain tissues has been performed previously; however, very limited data is available for children, which is particular important for modeling the pediatric traumatic brain injury (TBI). In this study, uniaxial compression tests at strain rates of 0.01/s, 1/s and 50/s up to 50% strain were performed for the corona radiata, corpus callosum, thalamus, cortex, cerebellum and brainstem of 8-week-old piglets. No significant difference in tissue strength was found among the cerebrum regions of cortex, thalamus, corona radiata and corpus callosum. The average stress of cerebellum was approximate 21% and 15% higher than that of cerebrum at a strain of 0.25 and 0.5, respectively, but it did not reach statistical significant level than most of the cerebrum regions. Brainstem was the stiffest among these 6 regions, and it was significant stiffer than most regions of cerebrum, with average stress of about 28% and 40% higher at a strain of 0.25 and 0.5, respectively. The strengths of all these three regions showed significant strain-rate dependent characteristics, with the strain rate increasing from 0.01/s to 50/s, the average stress of cerebrum, cerebellum and brainstem increased to approximate 4.6, 6.3 and 6.3 times, respectively at a strain of 0.25; and increased to approximate 1.9, 2.6, and 2.5 times, respectively at a strain of 0.5. One-term Ogden model was used to fit the experimental data to obtain the material parameters and numerical simulation was performed on the compression of cerebrum specimen. Results show that the constitutive model and the calibrated parameters can well represent the compressive behavior of the brain tissue at different strain rates. The results of this study are useful for developing biofidelic pediatric brain FE models and further predict the brain injuries under impact conditions.