Viscoelasticity of brain corpus callosum in biaxial tension
Viscoelasticity of brain corpus callosum in biaxial tension
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DOI：
10.1016/j.jmps.2016.08.010
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发表时间：
2016-11-01
影响因子：
5.3
通讯作者：
Puttlitz, Christian M.
中科院分区：
文献类型：
--
作者：
Labus, Kevin M.;Puttlitz, Christian M.
脑的计算模型依赖于精确的本构关系来模拟脑组织的粘弹性行为。目前的粘弹性模型是从一次在单一方向上进行的实验中推导出来的，因此缺乏关于多轴载荷效应的信息。目前还不清楚脑组织的时间依赖性行为是否取决于应变大小或受到拉伸应力时的加载方向。因此，从新鲜羊脑中分离的胼胝体组织进行双轴应力松弛和循环实验。结果表明，松弛行为是独立的应变大小，准线性粘弹性（QLV）模型能够准确地拟合实验数据。此外，各向同性约化松弛张量足以模拟轴突和横向方向的应力松弛。QLV模型拟合平均应力松弛试验在五个应变幅值，同时使用测得的应变历史从实验。由此产生的模型是能够准确地预测在两个应变幅值的循环试验的应力。除了从平均实验数据导出本构模型之外，还分别拟合每个试样，并报告模型参数的所得分布，并用于概率分析，以确定模型预测的概率分布和模型对参数方差的敏感性。这些结果可用于改进脑的计算研究中使用的粘弹性本构模型。(C)2016爱思唯尔有限公司版权所有
Computational models of the brain rely on accurate constitutive relationships to model the viscoelastic behavior of brain tissue. Current viscoelastic models have been derived from experiments conducted in a single direction at a time arid therefore lack information on the effects of multiaxial loading. It is also unclear if the time-dependent behavior of brain tissue is dependent on either strain magnitude or the direction of loading when subjected to tensile stresses. Therefore, biaxial stress relaxation and cyclic experiments were conducted on corpus callosum tissue isolated from fresh ovine brains. Results demonstrated the relaxation behavior to be independent of strain magnitude, and a quasi linear viscoelastic (QLV) model was able to accurately fit the experimental data. Also, an isotropic reduced relaxation tensor was sufficient to model the stress-relaxation in both the axonal and transverse directions. The QLV model was fitted to the averaged stress relaxation tests at five strain magnitudes while using the measured strain history from the experiments. The resulting model was able to accurately predict the stresses from cyclic tests at two strain magnitudes. In addition to deriving a constitutive model from the averaged experimental data, each specimen was fitted separately and the resulting distributions of the model parameters were reported and used in a probabilistic analysis to determine the probability distribution of model predictions and the sensitivity of the model to the variance of the parameters. These results can be used to improve the viscoelastic constitutive models used in computational studies of the brain. (C) 2016 Elsevier Ltd. All rights reserved.