Department of Mechanical Engineering, University of Birmingham, Birmingham, B15 2TT, UK.
Ann Biomed Eng. 2021 Dec;49(12):3737-3747. doi: 10.1007/s10439-021-02866-0. Epub 2021 Oct 4.
The mechanical characterization of brain tissue has been generally analyzed in the frequency and time domain. It is crucial to understand the mechanics of the brain under realistic, dynamic conditions and convert it to enable mathematical modelling in a time domain. In this study, the compressive viscoelastic properties of brain tissue were investigated under time and frequency domains with the same physical conditions and the theory of viscoelasticity was applied to estimate the prediction of viscoelastic response in the time domain based on frequency-dependent mechanical moduli through Finite Element models. Storage and loss modulus were obtained from white and grey matter, of bovine brains, using dynamic mechanical analysis and time domain material functions were derived based on a Prony series representation. The material models were evaluated using brain testing data from stress relaxation and hysteresis in the time dependent analysis. The Finite Element models were able to represent the trend of viscoelastic characterization of brain tissue under both testing domains. The outcomes of this study contribute to a better understanding of brain tissue mechanical behaviour and demonstrate the feasibility of deriving time-domain viscoelastic parameters from frequency-dependent compressive data for biological tissue, as validated by comparing experimental tests with computational simulations.
脑组织的力学特性通常在频域和时域中进行分析。了解真实动态条件下大脑的力学特性并将其转换为在时域中进行数学建模至关重要。在这项研究中,使用相同的物理条件在时域和频域中研究了脑组织的粘弹性压缩特性,并应用粘弹性理论通过有限元模型来估计基于频率相关力学模量的粘弹性响应的预测。使用动态力学分析从牛脑的白质和灰质中获得了存储和损耗模量,并基于 Prony 级数表示导出了时域材料函数。使用基于时间的分析中的应力松弛和滞后的脑测试数据来评估材料模型。有限元模型能够代表两种测试域中脑组织粘弹性特征的趋势。这项研究的结果有助于更好地了解脑组织的力学行为,并展示了从频域压缩数据推导出生物组织的时域粘弹性参数的可行性,这通过将实验测试与计算模拟进行比较得到了验证。