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An anisotropic hyperelastic constitutive model of brain white matter in biaxial tension and structural-mechanical relationships

机译:双轴张力脑白质的各向异性超塑性本构模型及结构机械关系

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Computational models of the brain require accurate and robust constitutive models to characterize the mechanical behavior of brain tissue. The anisotropy of white matter has been previously demonstrated; however, there is a lack of data describing the effects of multi-axial loading, even though brain tissue experiences multi-axial stress states. Therefore, a biaxial tensile experiment was designed to more fully characterize the anisotropic behavior of white matter in a quasi-static loading state, and the mechanical data were modeled with an anisotropic hyperelastic continuum model. A probabilistic analysis was used to quantify the uncertainty in model predictions because the mechanical data of brain tissue can show a high degree of variability, and computational studies can benefit from reporting the probability distribution of model responses. The axonal structure in white matter can be heterogeneous and regionally dependent, which can affect computational model predictions. Therefore, corona radiata and corpus callosum regions were tested, and histology and transmission electron microscopy were performed on tested specimens to relate the distribution of axon orientations and the axon volume fraction to the mechanical behavior. These measured properties were implemented into a structural constitutive model. Results demonstrated a significant, but relatively low anisotropic behavior, yet there were no conclusive mechanical differences between the two regions tested. The inclusion of both biaxial and uniaxial tests in model fits improved the accuracy of model predictions. The mechanical anisotropy of individual specimens positively correlated with the measured axon volume fraction, and, accordingly, the structural model exhibited slightly decreased uncertainty in model predictions compared to the model without structural properties. (C) 2016 Elsevier Ltd. All rights reserved.
机译:大脑的计算模型需要准确且坚固的本构模型来表征脑组织的机械行为。以前证明了白质的各向异性;然而,即使脑组织经历多轴应力状态,缺乏描述多轴加载的效果的数据。因此,设计了双轴拉伸实验以更充分地表征在准静态加载状态下白质的各向异性行为,并且使用各向异性超弹性连续体模型进行机械数据。概率分析用于量化模型预测中的不确定性,因为脑组织的机械数据可以显示出高度的可变性,并且计算研究可以从报告模型反应的概率分布中受益。白质中的轴突结构可以是异构的和区域依赖性的,这可能影响计算模型预测。因此,测试了电晕radiata和胼call软骨区域,并对测试的试样进行了组织学和透射电子显微镜,以涉及轴突取向的分布和轴突体积分数与机械行为。这些测量性质被实施为结构本构模型。结果表明了一个显着但相对较低的各向异性行为,但两种区域之间没有确凿的机械差异。在模型中包含双轴和单轴测试符合模型预测的准确性。与测量的轴突体积分数呈正相关的单个样本的机械各向异性,并且相应地,与没有结构性质的模型相比,结构模型在模型预测中表现出略微降低的不确定性。 (c)2016 Elsevier Ltd.保留所有权利。

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