Zhao Wei, Ji Songbai
Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01605, USA.
Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.
Biomech Model Mechanobiol. 2017 Apr;16(2):449-461. doi: 10.1007/s10237-016-0829-7. Epub 2016 Sep 19.
Head angular velocity, instead of acceleration, is more predictive of brain strains. Surprisingly, no study exists that investigates how shape variation in angular velocity profiles affects brain strains, beyond characteristics such as peak magnitude and impulse duration. In this study, we evaluated brain strain uncertainty due to variation in angular velocity profiles and further compared with that resulting from simplifying the profiles into idealized shapes. To do so, we used reconstructed head impacts from American National Football League for shape extraction and simulated head uniaxial coronal rotations from onset to full stop. The velocity profiles were scaled to maintain an identical peak velocity magnitude and duration in order to isolate the shape for investigation. Element-wise peak maximum principal strains from 44 selected impacts were obtained. We found that the shape of angular velocity profile could significantly affect brain strain magnitude (e.g., percentage difference of 4.29-17.89 % in the whole brain relative to the group average, with cumulative strain damage measure (CSDM) uncertainty range of 23.9 %) but not pattern (correlation coefficient of 0.94-0.99). Strain differences resulting from simplifying angular velocity profiles into idealized shapes were largely within the range due to shape variation, in both percentage difference and CSDM (signed difference of 3.91 % on average, with a typical range of 0-6 %). These findings provide important insight into the uncertainty or confidence in the performance of kinematics-based injury metrics. More importantly, they suggest the feasibility to simplify head angular velocity profiles into idealized shapes, at least within the confinements of the profiles evaluated, to enable real-time strain estimation via pre-computation in the future.
头部角速度而非加速度,对脑应变的预测性更强。令人惊讶的是,除了峰值大小和脉冲持续时间等特征外,尚无研究调查角速度剖面的形状变化如何影响脑应变。在本研究中,我们评估了由于角速度剖面变化导致的脑应变不确定性,并进一步与将剖面简化为理想化形状所产生的不确定性进行了比较。为此,我们使用了美国国家橄榄球联盟重建的头部撞击数据进行形状提取,并模拟了从开始到完全停止的头部单轴冠状旋转。为了分离出用于研究的形状,我们对标定速度剖面以保持相同的峰值速度大小和持续时间。获得了44次选定撞击的逐元素峰值最大主应变。我们发现,角速度剖面的形状可显著影响脑应变大小(例如,相对于组平均值,全脑的百分比差异为4.29 - 17.89%,累积应变损伤测量(CSDM)的不确定性范围为23.9%),但不影响模式(相关系数为0.94 - 0.99)。将角速度剖面简化为理想化形状所导致的应变差异,在百分比差异和CSDM方面,很大程度上都在形状变化所致的范围内(平均符号差异为3.91%,典型范围为0 - 6%)。这些发现为基于运动学的损伤指标性能的不确定性或置信度提供了重要见解。更重要的是,它们表明将头部角速度剖面简化为理想化形状是可行的,至少在所评估剖面的范围内是可行的,以便未来通过预计算实现实时应变估计。