Wu Shaoju, Zhao Wei, Wu Zheyang, McAllister Thomas, Hu Jingwen, Ji Songbai
Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01609, USA.
Mathematical Sciences, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
Biomech Model Mechanobiol. 2023 Feb;22(1):159-175. doi: 10.1007/s10237-022-01638-6. Epub 2022 Oct 6.
Most human head/brain models represent a generic adult male head/brain. They may suffer in accuracy when investigating traumatic brain injury (TBI) on a subject-specific basis. Subject-specific models can be developed from neuroimages; however, neuroimages are not typically available in practice. In this study, we establish simple and elegant regression models between brain outer surface morphology and head dimensions measured from neuroimages along with age and sex information (N = 191; 141 males and 50 females with age ranging 14-25 years). The regression models are then used to approximate subject-specific brain models by scaling a generic counterpart, without using neuroimages. Model geometrical accuracy is assessed using adjusted [Formula: see text] and absolute percentage error (e.g., 0.720 and 3.09 ± 2.38%, respectively, for brain volume when incorporating tragion-to-top). For a subset of 11 subjects (from smallest to largest in brain volume), impact-induced brain strains are compared with those from "morphed models" derived from neuroimage-based mesh warping. We find that regional peak strains from the scaled subject-specific models are comparable to those of the morphed counterparts but could be considerably different from those of the generic model (e.g., linear regression slope of 1.01-1.03 for gray and white matter regions versus 1.16-1.19, or up to ~ 20% overestimation for the smallest brain studied). These results highlight the importance of incorporating brain morphological variations in impact simulation and demonstrate the feasibility of approximating subject-specific brain models without neuroimages using age, sex, and easily measurable head dimensions. The scaled models may improve subject specificity for future TBI investigations.
大多数人体头部/脑部模型代表的是普通成年男性的头部/脑部。在基于个体进行创伤性脑损伤（TBI）研究时，它们的准确性可能会受到影响。可以从神经影像中开发个体特异性模型；然而，在实际应用中通常无法获取神经影像。在本研究中，我们建立了脑外表面形态与从神经影像测量得到的头部尺寸以及年龄和性别信息（N = 191；141名男性和50名女性，年龄范围为14 - 25岁）之间简单而精确的回归模型。然后，在不使用神经影像的情况下，通过对通用模型进行缩放，利用这些回归模型来近似个体特异性脑模型。使用调整后的[公式：见正文]和绝对百分比误差来评估模型的几何精度（例如，纳入外耳道至头顶距离时，脑体积的误差分别为0.720和3.09±2.38%）。对于11名受试者的子集（按脑体积从小到大），将撞击引起的脑应变与基于神经影像的网格变形得到的“变形模型”的脑应变进行比较。我们发现，缩放后的个体特异性模型的区域峰值应变与变形对应模型的相当，但可能与通用模型的有很大差异（例如，灰质和白质区域的线性回归斜率为1.01 - 1.03，而通用模型为1.16 - 1.19，对于研究的最小脑体积，高估可达约20%）。这些结果突出了在撞击模拟中纳入脑形态变化的重要性，并证明了在不使用神经影像的情况下，利用年龄、性别和易于测量的头部尺寸来近似个体特异性脑模型的可行性。缩放后的模型可能会提高未来TBI研究的个体特异性。
