Liu Jiacheng, Jin Jionghua Judy, Eckner James T, Ji Songbai, Hu Jingwen
Department of Industry & Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
Department of Physical Medicine & Rehabilitation, University of Michigan, Ann Arbor, MI, USA.
J Biomech. 2022 Apr;135:111036. doi: 10.1016/j.jbiomech.2022.111036. Epub 2022 Mar 11.
Tissue-level brain responses to sport-related head impacts may be stronger predictors of brain injury risk than head kinematics alone. Despite the importance of accurate impact response estimation, the influence of head morphological variations has not been properly considered due to the limited sizes and shapes of existing computational head models. In this study, we developed 101 subject-specific finite element (FE) head-brain models based on CT scans and a parametric modeling approach to estimate tissue-level brain impact responses (maximal principal strain, MPS) under three head impact conditions. Principal component analysis (PCA) was used to quantify the geometric variations, with statistically significant PCs then selected to predict MPS using a stepwise linear regression model. High adjusted R values (0.6-0.9) were achieved in the regression model, suggesting a good model predictability. Brain volume explained the largest variance of 51.3%, and it was highly correlated with MPS, indicating a significant size effect on brain impact responses. This is the first modeling study to systematically consider the influence of morphological variations in the inner skull and scalp on brain tissue impact response.
与仅考虑头部运动学相比，组织水平的大脑对与运动相关的头部撞击的反应可能是脑损伤风险更强的预测指标。尽管准确估计撞击反应很重要，但由于现有计算头部模型的尺寸和形状有限，头部形态变化的影响尚未得到充分考虑。在本研究中，我们基于CT扫描和参数化建模方法开发了101个特定于个体的有限元（FE）头部-大脑模型，以估计三种头部撞击条件下组织水平的大脑撞击反应（最大主应变，MPS）。主成分分析（PCA）用于量化几何变化，然后选择具有统计学意义的主成分，使用逐步线性回归模型预测MPS。回归模型获得了较高的调整后R值（0.6-0.9），表明模型具有良好的预测能力。脑容量解释了最大的方差，为51.3%，并且它与MPS高度相关，表明尺寸对大脑撞击反应有显著影响。这是第一项系统考虑内颅骨和头皮形态变化对脑组织撞击反应影响的建模研究。
