Bio-mathematical Models of Aggregated Tissues & Organ Properties: Extension to Brain Tissue

Corvid Technologies, LLC and Illinois Institute of Technology (IIT) propose to build upon the experimental characterization and mathematical modeling techniques developed for cardiac tissue transition boundaries to improve the state of human brain tissues modeling. The approach will focus on the development of blended tissue models for brain tissue to improve the modeling accuracy for neurosurgical simulators and injury biomechanics models used for human body injury assessment. The proposed effort seeks to characterize the transitional brain tissues of the meninges and cerebral vasculature, components which are often neglected in high-fidelity head finite element models but are critical to the isolation and stabilization of the brain within the skull. Investigation of additional transition areas within the brain which are commonly affected at the onset of brain injury (grey-white matter transition, myelin-axon interface, and microvasculature) will also be undertaken with the goal of developing multi-scale damage models able to simulate changes to the tissue nanostructure using conventional finite element methods. Corvid and IIT are uniquely positioned to efficiently accomplish the objectives of this proposal due to our previous efforts establishing X-ray diffraction (XRD) based methodologies for characterization of tissue transition dynamics, years of experience in modeling the human body response to external stimuli, advanced injury prediction capability using whole body finite element models, and in-house, high performance computing (HPC) resources and software development capabilities. X-ray diffraction scanning will be once again utilized to record the mechanical response across the brain tissue boundaries under load with minimal adverse effect or outside influence on the tissue specimen. Functionally graded mathematical models will be developed and validated for the brain tissue transition regions based upon the experimental results. Previous studies in the Orgel Lab have shown changes to the myelin X-ray diffraction patterns as a function of tissue damage. This information will be utilized to characterize and measure damage to brain tissue nanostructures with applied load, resulting in an experimental dataset to be used for multi-scale brain tissue damage modeling. The combination of novel XRD based experimental techniques and blended tissue modeling tools applied to the brain tissue transitions will result in a unique, high-fidelity finite element model of the brain which may help researchers in the field of neuroscience gain a more realistic understanding of the mechanical response and injury risk to the brain during surgical procedures or head impact events.