Abstract
Brain tissue is one of the softest tissues in the human body and the quantification of its mechanical properties has challenged scientists over the past decades. Associated experimental results in the literature have been contradictory as characterizing the mechanical response of brain tissue not only requires well-designed experimental setups that can record the ultrasoft response, but also appropriate approaches to analyze the corresponding data. Due to the extreme complexity of brain tissue behavior, nonlinear continuum mechanics has proven an expedient tool to analyze testing data and predict the mechanical response using a combination of hyper-, visco-, or poro-elastic models. Such models can not only allow for personalized predictions through finite element simulations, but also help to comprehensively understand the physical mechanisms underlying the tissue response. Here, we use a nonlinear poro-viscoelastic computational model to evaluate the effect of different intrinsic material properties (permeability, shear moduli, nonlinearity, viscosity) on the tissue response during different quasi-static biomechanical measurements, i.e., large-strain compression and tension as well as indentation experiments. We show that not only the permeability but also the properties of the viscoelastic solid largely control the fluid flow within and out of the sample. This reveals the close coupling between viscous and porous effects in brain tissue behavior. Strikingly, our simulations can explain why indentation experiments yield that white matter tissue in the human brain is stiffer than gray matter, while large-strain compression experiments show the opposite trend. These observations can be attributed to different experimental loading and boundary conditions as well as assumptions made during data analysis. The present study provides an important step to better understand experimental data previously published in the literature and can help to improve experimental setups and data analysis for biomechanical testing of brain tissue in the future.
Highlights
We have used a poro-viscoelastic computational model for brain tissue behavior to systematically analyze the viscous and porous contributions to the quasi-static response recorded during common biomechanical testing setups, i.e., large-strain compression and tension as well as indentation experiments
We have demonstrated the effects of the initial intrinsic permeability, shear moduli, nonlinearity, and viscosity on the test-setup-dependent recorded mechanical response and associated read-outs
Our analyses allow us to evaluate and explain differences in the reported data on human brain tissue mechanics that stem from poro-viscoelastic effects in combination with different drainage and loading conditions that differ greatly depending on the experimental procedure
Summary
It has increasingly been recognized that mechanical signals play an important role for brain development (Budday et al, 2015b; Koser et al, 2016; Thompson et al, 2019), injury (Meaney et al, 2014; Hemphill et al, 2015; Keating and Cullen, 2021), and disease (Murphy et al, 2016; Barnes et al, 2017; Gerischer et al, 2018; Park et al, 2018). In silico modeling based on the theory of nonlinear continuum mechanics has proven a valuable tool to, on the one hand, computationally test hypotheses that complement experimental studies and provide a predictive understanding of processes in the brain under physiological and pathological conditions (Goriely et al, 2015; Budday et al, 2020). Brain tissue is ultrasoft—arguably softer than any other tissue in the human body—and deforms noticeably when it is taken out of its physiological environment within the skull, e.g., for ex vivo mechanical testing. It has an exceptionally high water content, 0.83 g/ml in gray matter and 0.71 g/ml in white matter (Whittall et al, 1997). It is difficult to control specimen geometry, local deformation states, and their relation to the recorded forces (Rashid et al, 2012)
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