Mechanical aspects play an important role in brain development, function, and disease. Therefore, continuum-mechanics-based computational models are a valuable tool to advance our understanding of mechanics-related physiological and pathological processes in the brain. Currently, mainly phenomenological material models are used to predict the behavior of brain tissue numerically. The model parameters often lack physical interpretation and only provide adequate estimates for brain regions which have a similar microstructure and age as those used for calibration. These issues can be overcome by establishing advanced constitutive models that are microstructurally motivated and account for regional heterogeneities through microstructural parameters.

In this work, we perform simultaneous compressive mechanical loadings and microstructural analyses of porcine brain tissue to identify the microstructural mechanisms that underlie the macroscopic nonlinear and time-dependent mechanical response. Based on experimental insights into the link between macroscopic mechanics and cellular rearrangements, we propose a microstructure-informed finite viscoelastic constitutive model for brain tissue. We determine a relaxation time constant from cellular displacement curves and introduce hyperelastic model parameters as linear functions of the cell density, as determined through histological staining of the tested samples. The model is calibrated using a combination of cyclic loadings and stress relaxation experiments in compression. The presented considerations constitute an important step towards microstructure-based viscoelastic constitutive models for brain tissue, which may eventually allow us to capture regional material heterogeneities and predict how microstructural changes during development, aging, and disease affect macroscopic tissue mechanics.

机械方面在大脑发育，功能和疾病中起重要作用。因此，基于连续力学的计算模型是提高我们对大脑中与力学相关的生理和病理过程的理解的有价值的工具。当前，主要的现象学材料模型用于数字地预测脑组织的行为。模型参数通常缺乏物理解释，只能提供对大脑区域的足够估计，这些区域的微观结构和年龄与用于校准的相似。这些问题可以通过建立先进的本构模型来克服，这些本构模型是微结构化的，并通过微结构参数来说明区域异质性。

在这项工作中，我们同时进行猪脑组织的压缩机械载荷和微观结构分析，以识别构成宏观非线性和时间依赖性机械响应基础的微观结构机制。基于对宏观力学和细胞重排之间的联系的实验见解，我们提出了一种针对脑组织的微结构信息化有限粘弹性本构模型。我们通过细胞位移曲线确定弛豫时间常数，并引入超弹性模型参数作为细胞密度的线性函数，这是通过对测试样品进行组织学染色确定的。使用循环载荷和压缩中的应力松弛实验的组合对模型进行校准。