Abstract
Head finite element models (hFEMs) are instrumental in understanding injury mechanisms in head impacts. Personalizing hFEMs is crucial for capturing individualized brain responses, with brain volume scaling proving effective. However, the role of refined white matter (WM) segmentation in hFEMs for studying repetitive subconcussive head impacts (rSHIs) in American football remains underexplored. This study evaluated the effect of refined WM segmentation of 34 WM segments on responses variability due to brain volume variations, using peak maximum principal strain (95MPS) and strain rate (95MPSr) as injury predictive metrics. Data from diffusion-weighted imaging (DWI) of 21 Canadian varsity football players were utilized to personalize 21 hFEMs. Simulating four different head impacts, representing 50th and 99th percentile resultant accelerations in frontal and top-right directions, refined WM segmentation better captured variability of strain responses compared to baseline segmentation. Up to 94.76% of 95MPS and 99.05% of 95MPSr responses were significantly different across refined WM segments for players, compared to a maximum of 12.86% of responses with baseline segmentation. This highlights the necessity of refined WM segmentation for capturing player-specific responses. Both impact direction and intensity influenced strain response variations, with lower intensity and frontal impacts showing greater player-specificity. These findings emphasize the importance of refined WM segmentation in hFEMs for comprehensively evaluating strain responses under rSHI. Detailed WM segmentation in hFEMs is crucial for comprehensive injury assessment, enhancing the alignment of hFEMs with imaging studies evaluating changes in WM integrity across segments. The simple and straightforward method presented herein to achieve player-specific strain response is promising for future rSHI studies.