Zhou Zhou, Fahlstedt Madelen, Li Xiaogai, Kleiven Svein
Neuronic Engineering, KTH Royal Institute of Technology, 14152, Stockholm, Sweden.
Mips AB, Täby, Sweden.
Ann Biomed Eng. 2025 Mar;53(3):699-717. doi: 10.1007/s10439-024-03653-3. Epub 2024 Dec 5.
Traumatic brain injury (TBI) in cyclists is a growing public health problem, with helmets being the major protection gear. Finite element head models have been increasingly used to engineer safer helmets often by mitigating brain strain peaks. However, how different helmets alter the spatial distribution of brain strain remains largely unknown. Besides, existing research primarily used maximum principal strain (MPS) as the injury parameter, while white matter fiber tract-related strains, increasingly recognized as effective predictors for TBI, have rarely been used for helmet evaluation. To address these research gaps, we used an anatomically detailed head model with embedded fiber tracts to simulate fifty-one helmeted impacts, encompassing seventeen bicycle helmets under three impact locations. We assessed the helmet performance based on four tract-related strains characterizing the normal and shear strain oriented along and perpendicular to the fiber tract, as well as the prevalently used MPS. Our results showed that both the helmet model and impact location affected the strain peaks. Interestingly, we noted that different helmets did not alter strain distribution, except for one helmet under one specific impact location. Moreover, our analyses revealed that helmet ranking outcome based on strain peaks was affected by the choice of injury metrics (Kendall's Tau coefficient: 0.58-0.93). Significant correlations were noted between tract-related strains and angular motion-based injury metrics. This study provided new insights into computational brain biomechanics and highlighted the helmet ranking outcome was dependent on the choice of injury metrics. Our results also hinted that the performance of helmets could be augmented by mitigating the strain peak and optimizing the strain distribution with accounting the selective vulnerability of brain subregions and more research was needed to develop region-specific injury criteria.
骑自行车者的创伤性脑损伤（TBI）是一个日益严重的公共卫生问题，头盔是主要的防护装备。有限元头部模型越来越多地用于设计更安全的头盔，通常是通过减轻脑应变峰值来实现。然而，不同头盔如何改变脑应变的空间分布在很大程度上仍不清楚。此外，现有研究主要使用最大主应变（MPS）作为损伤参数，而白质纤维束相关应变越来越被认为是TBI的有效预测指标，但很少用于头盔评估。为了填补这些研究空白，我们使用了一个包含纤维束的解剖学详细头部模型来模拟51次戴头盔的撞击，涵盖了17种自行车头盔在三个撞击位置的情况。我们基于四种与纤维束相关的应变来评估头盔性能，这些应变表征了沿纤维束方向和垂直于纤维束方向的法向应变和剪应变，以及普遍使用的MPS。我们的结果表明，头盔模型和撞击位置都会影响应变峰值。有趣的是，我们注意到除了一个头盔在一个特定撞击位置的情况外，不同头盔并没有改变应变分布。此外，我们的分析表明，基于应变峰值的头盔排名结果受损伤指标选择的影响（肯德尔tau系数：0.58 - 0.93）。在与纤维束相关的应变和基于角运动的损伤指标之间发现了显著相关性。这项研究为计算脑生物力学提供了新的见解，并强调了头盔排名结果取决于损伤指标的选择。我们的结果还暗示，可以通过减轻应变峰值和优化应变分布来提高头盔性能，同时考虑脑亚区域的选择性易损性，并且需要更多研究来制定区域特异性损伤标准。
