In the last decade, computational models of the brain have become the gold standard tool for investigating traumatic brain injury (TBI) mechanisms and developing novel protective equipment and other safety countermeasures. However, most studies utilizing finite element (FE) models of the brain have been conducted using models developed to represent the average neuroanatomy of a target demographic, such as the 50th percentile male. Although this is an efficient strategy, it neglects normal anatomical variations present within the population and their contributions on the brain's deformation response. As a result, the contributions of structural characteristics of the brain, such as brain volume, on brain deformation are not well understood. The objective of this study was to develop a set of statistical regression models relating measures of the size and shape of the brain to the resulting brain deformation. This was performed using a database of 125 subject-specific models, simulated under six independent head kinematic boundary conditions, spanning a range of impact modes (frontal, oblique, side), severity (non-injurious and injurious), and environments (volunteer, automotive, and American football). Two statistical regression techniques were utilized. First, simple linear regression (SLR) models were trained to relate intracranial volume (ICV) and the 95th percentile of maximum principal strain (MPS-95) for each of the impact cases. Second, a partial least squares regression model was constructed to predict MPS-95 based on the affine transformation parameters from each subject, representing the size and shape of their brain, considering the six impact conditions collectively. Both techniques indicated a strong linear relationship between ICV and MPS-95, with MPS-95 varying by approximately 5% between the smallest and largest brains. This difference represented up to 40% of the mean strain across all subjects. This study represents a comprehensive assessment of the relationships between brain anatomy and deformation, which is crucial for the development of personalized protective equipment, identifying individuals at higher risk of injury, and using computational models to aid clinical diagnostics of TBI.
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