Quantifying the Effect of Sex-Related Differences in Biomechanical Factors on Brain Deformation Response

Reynier, Kristen, Mechanical and Aerospace Engineering - School of Engineering and Applied Science, University of Virginia
Panzer, Matthew, EN-Mech & Aero Engr Dept, University of Virginia
Crandall, Jeff, EN-Mech & Aero Engr Dept, University of Virginia
Forman, Jason, EN-Center for Applied Biomechanics (CAB), University of Virginia
Broshek, Donna, Psychiatry and Neurobehavioral Sciences, University of Virginia
Chernyavskiy, Pavel, MD-PBHS Public Health Sciences Admin, University of Virginia

Traumatic brain injuries (TBIs) are complex injuries resulting in a variety of symptoms, disabilities, or even death, with common causes including motor vehicle crashes, sports, or unintentional falls. For belted automotive occupants in frontal crashes, females have a significantly greater risk of sustaining a moderate brain injury after controlling for covariates such as age, height, body mass index, delta-V of the collision, and vehicle model year. There are physical differences between males and females which may influence the biomechanics of the brain during a crash and explain the disparity of brain injury risk between the sexes. Biomechanical factors such as neuroanatomy, material properties, and the resulting head kinematics caused by the event all contribute to the severity of brain deformation, the primary mechanism of diffuse TBI. Computational finite element (FE) brain models are commonly used to predict brain response under potentially injurious loading, but a majority of research has focused on mid-sized adult males, and additional research is required to determine the effect of sex on brain deformation using these FE brain models. Therefore, the goal of this dissertation is to quantify sex-related differences in neuroanatomy, material properties, and head kinematics and their effect on brain deformation response in automotive loading environments using subject-specific FE brain models.
A previously developed registration-based morphing framework was utilized to develop subject-specific FE brain models from magnetic resonance imaging and elastography brain scans to capture unique neuroanatomies and material property features. For each of the subjects, intrinsic biomechanical features were evaluated and included measures of brain volumes, shear stiffnesses, and damping ratios. Sex was a significant predictor for many of these intrinsic biomechanical features, such as intracranial volume (ICV) and mean damping ratio, as estimated the Bayesian linear mixed model. The subject-specific FE models were then simulated using head kinematics from an oblique frontal sled test, and the biomechanical features and sex were assessed to determine their effect on the brain tissue deformation metrics. Of the neuroanatomical features, ICV had the greatest effect on brain tissue maximum principal strain, and damping ratio had the greatest effect of the material properties; however, sex did not have a significant effect on the deformation metrics tested based on the Bayesian linear mixed model.
Based on differences in mass distribution and engagement with restraint systems, the effect of sex on automotive crash head kinematics needs to be considered. For sex-matched sled tests of post-mortem human surrogates and anthropometric test devices, sex was a significant predictor of peak kinematic metrics; however, the surrogate’s sex did not have a significant effect on predicting brain deformation after including these kinematic features. However, the number of sex-matched sled test environments was limited, and there are often differences in experimental restraint systems (e.g., shoulder belt load limiters). Potential differences in head kinematics, as a result of these inconsistent test environment, have not been assessed.
A final sensitivity study assessed which of the biomechanical features within the neuroanatomy study, material properties study, and the head kinematics study had the greatest impact on brain deformation. Overall, biomechanical factors associated with the head kinematics had the overall greatest effect, and sex did not have a statistically significant effect on brain deformation. Additionally, ICV had a statistically significant effect on all deformation metrics.
The research presented in this dissertation provides an analysis of how biomechanical features within a subject’s neuroanatomy, material properties, and head kinematics affect brain deformation. Ultimately, the outcomes presented in this dissertation can direct future work to address areas with the greatest impact on brain deformation to reduce injury risk in both male and female automotive occupants.

PHD (Doctor of Philosophy)
Brain biomechanics, Sex differences
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