Center for Applied Biomechanics, Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, Virginia, USA.
Department of Radiology and Medical Imaging, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
J Neurotrauma. 2023 Aug;40(15-16):1796-1807. doi: 10.1089/neu.2022.0339. Epub 2023 Jun 15.
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.
在过去的十年中，大脑计算模型已成为研究创伤性脑损伤（TBI）机制、开发新型保护设备和其他安全对策的金标准工具。然而，大多数利用大脑有限元（FE）模型的研究都是使用代表目标人群平均神经解剖结构的模型进行的，例如 50 百分位数男性。尽管这是一种有效的策略，但它忽略了人群中存在的正常解剖变异及其对大脑变形响应的贡献。因此，大脑的结构特征，如脑容量，对大脑变形的贡献尚不清楚。本研究的目的是开发一组统计回归模型，将大脑大小和形状的测量值与大脑的变形结果联系起来。这是通过使用 125 个特定于主题的模型数据库来完成的，这些模型是在六个独立的头部运动学边界条件下模拟的，涵盖了各种冲击模式（正面、斜向、侧面）、严重程度（非损伤和损伤）和环境（志愿者、汽车和美式足球）。使用了两种统计回归技术。首先，训练简单线性回归（SLR）模型以将颅内体积（ICV）和最大主应变（MPS-95）的第 95 百分位数相关联，对于每个冲击案例。其次，构建了偏最小二乘回归模型，根据每个受试者的仿射变换参数预测 MPS-95，代表其大脑的大小和形状，同时考虑到六个冲击条件。这两种技术都表明 ICV 和 MPS-95 之间存在很强的线性关系，最小和最大大脑之间的 MPS-95 差异约为 5%。这种差异代表了所有受试者平均应变的 40%。本研究代表了对大脑解剖结构和变形之间关系的全面评估，这对于开发个性化保护设备、识别受伤风险较高的个体以及使用计算模型辅助 TBI 的临床诊断至关重要。
