Insights into the Microstructural Origin of Brain Viscoelasticity

Abstract

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.

Poro-Viscoelastic Effects During Biomechanical Testing of Human Brain Tissue

“…While the latter is evidently the main determinant in the fluid part of the biphasic response, (Figures 5, 6), interestingly, also different combinations of the solid parameters μ 0 ∞ , μ 0 1 , α, and η have a noticeable effect (see fluid nominal stress in Figure 8, pore pressure in Figure 9 and seepage velocity in Figure 10). These observations agree well with our previous findings (Reiter et al, 2021), showing that cells inside brain tissue still keep moving in the direction of loading during the holding period of compression relaxation experiments-only with decreasing velocity. This further supports the idea that the porous and viscous contributions to the response of brain tissue are strongly coupled, i.e., the moving fluid might exert a drag force on cell bodies and thereby displace them.…”

Unraveling the Local Relation Between Tissue Composition and Human Brain Mechanics Through Machine Learning

“…Viscoelastic effects seem to depend in particular on all cellular proteins, GFAP, MBP and Iba1, even though the absolute quantities of these proteins were rather low in all samples. This observation also agrees with our previous findings (Reiter et al, 2021), where we could show that the network of intercellular connections behaves viscoelastically. Interestingly, GFAP showed a higher relevance for the viscoelastic than for the quasi-elastic response of the tissue, which further supports the importance of the cellular network for brain viscoelasticity.…”

“…Eventually, they could even be used to assist diagnosis and treatment of neurological disorders or the detailed planning of surgical procedures (Weickenmeier et al, 2017a;Zarzor et al, 2021). In this respect, understanding the link between microstructure and mechanics of brain tissue can help to develop more realistic material models that capture local variations in tissue properties (Budday et al, 2020b;Reiter et al, 2021).…”

“…While first studies have successfully incorporated distinct microstructural parameters into analytic constitutive laws for brain tissue (Budday et al, 2020b: Reiter et al, 2021, data-driven approaches such as machine learning bear the potential to open up a much more comprehensive view. First attempts to use machine learning for relating tissue microstructure to macroscopic mechanical properties used simple end-to-end model architectures (Liang et al, 2017).…”

Spinal Cord Neuronal Network Formation in a 3D Printed Reinforced Matrix—A Model System to Study Disease Mechanisms

“…The cellular environment in the CNS represents a particularly complex matrix with highly nonlinear and time-dependent material behavior. [38] A key challenge when conducting studies with neural tissue is to mimic the ultrasoft native CNS tissue with an elastic modulus of only a few hundred Pa. [30] Previously, it was shown by neuronal differentiation and maturation approaches that 3D cell cultures of neurons require ultraweak matrices to enhance neuronal differentiation and maturation. [39] Ultrasoft matrices alone, however, are unable to handle and take through the cell culture process or to allow transfer into an electrophysiological recording chamber.…”