Master of Engineering Science
Musculoskeletal Health Research
Sports related concussions and mild traumatic brain injuries have seen an increase in frequency over the past decade. The creation of highly biofidelic computational head models is an important step in understanding the mechanisms of these mild brain injuries and preventing them. Hence, the purpose of this research is to combine state-of-the-art computational models, brain imaging modalities and traditional head injury assessment protocols to simulate and predict the brains responses during traumatic head impacts. A novel, atlas-based, parcellated axon fiber embedded head model was developed which allows for in-depth analysis of the brain’s structural connectome tracts for injury diagnosis and analysis. New axon strain metrics were developed along with traditional head kinematic methodologies to create one of the most advanced finite element head models for concussion injury reconstruction which allows for comparison to patient symptoms through tract injury level prediction.
Summary for Lay Audience
With the ever-growing evidence of the major health risks associated with traumatic brain injuries and concussions, development of new methods for researching and diagnosing injury mechanism is required. Our lab is attempting to tackle this problem by incorporating finite element methods to the complex geometries and material properties of the human brain. This thesis was completed over the course of 2 years and begins with an exploration into the mechanisms that produce what are considered ‘signs’ of traumatic brain injuries. The work then progresses to examine some of the leading predictive injury criteria’s and assess their viability and limitations. Finally, this project led to the development of a new modified finite element head model and goes through the generation of parcellated fiber axon models that will help to better understand the injury mechanism of the brain’s communication neural network. This model, which currently encompasses 41 distinct fiber bundles, is, as of now, the only embedded finite element parcellated fiber axon model using group averaged diffuse tensor imaging data in the world.
Along with the development of the embedded and parcellated fiber axon model, a new injury prediction metric has been developed. Using the strains produced in the axial direction of the fibers, like previous cadaveric experiments, it is possible to determine the overall injury present in a specific fiber bundle as a percentage over a predetermined ‘injury’ threshold. This will allow for the comparison of different fiber tract damage under different dynamic impact scenarios.
The possibilities for future studies that look explore damage to specific fiber orientations, fiber lengths and fiber functionalities will allow for in-depth analysis of the inner mechanisms of the brain. The overarching goal of this research is to couple engineering principals with medical imaging techniques and neuroscience to understand, diagnose and prevent some of the symptoms and impairments associated with concussions and mild traumatic brain injuries.
Levy, Yanir, "Computational Modeling of the Human Brain for mTBI Prediction and Diagnosis" (2020). Electronic Thesis and Dissertation Repository. 7291.
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