Mechanical characterization of human brain tissue

Budday, Silvia; Sommer, Gerhard; Birkl, Christoph; Langkammer, Christian; Haybaeck, Johannes; Kohnert, J; Bauer, Melanie; Paulsen, Friedrich; Steinmann, Paul; Kuhl, Ellen; Holzapfel, Gerhard

doi:10.1016/j.actbio.2016.10.036

Cited by 443 publications

(511 citation statements)

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“…The nonlinear shear modulus of deterministic Ogden models with one or multiple terms was previously calibrated to the same mean values in [19]. Extensive experimental results for human brain tissue under combined shear and axial deformations were reported in [17], where the mean elastic responses were calculated as the average between the viscoelastic loading and unloading paths. The standard deviation considered here represents the range of viscoelastic responses.…”

Section: Example 3 (Human Brain)mentioning

confidence: 99%

“…First, we carry on the traditional method used in the deterministic approach [17][18][19]71]. That is, we determine the mean value of the nonlinear shear modulus (2.13) by minimising the residual…”

Section: (A) Calibration Of Random Field Parametersmentioning

confidence: 99%

“…For the random vector (R 1 (a 0 ), · · · , Rn(a 0 )), applying the constraints [23,25] 15) this vector follows a Dirichlet distribution [41,74], D (ξ 1 (a 0 ), · · · , ξn(a 0 )). Then, every random variable Rp(a 0 ), p = 1, · · · , n, follows a standard Beta distribution [40,41], B(ξp(a 0 ), ψp(a 0 )), with ξp(a 0 ) > 0 and ψp(a 0 ) = n q=1,q =p ξq(a 0 ) > 0 satisfying 17) and is calculated from the mean values obtained at step 1. Finally, the optimal hyperparameter vectors (ρ 1 (a 0 ), ρ 2 (a 0 )) and (ξ 1 (a 0 ), · · · , ξn(a 0 )) are identified by minimising the residual for the standard deviation (3.6), and taking into account relations (3.10), (3.13), and (3.16).…”

Section: (A) Calibration Of Random Field Parametersmentioning

confidence: 99%

“…For natural and engineered materials, uncertainties in the experimental observations typically arise from the inherent micro-structural inhomogeneity [13,14], sample-to-sample intrinsic variability, or when elastic data are extracted from viscoelastic mechanical tests [15][16][17][18][19]. For these materials, hyperelastic models based on mean data values constitute a starting point for the development of more complex models.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic isotropic hyperelastic materials: constitutive calibration and model selection

2018

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Biological and synthetic materials often exhibit intrinsic variability in their elastic responses under large strains, owing to microstructural inhomogeneity or when elastic data are extracted from viscoelastic mechanical tests. For these materials, although hyperelastic models calibrated to mean data are useful, stochastic representations accounting also for data dispersion carry extra information about the variability of material properties found in practical applications. We combine finite elasticity and information theories to construct homogeneous isotropic hyperelastic models with random field parameters calibrated to discrete mean values and standard deviations of either the stress-strain function or the nonlinear shear modulus, which is a function of the deformation, estimated from experimental tests. These quantities can take on different values, corresponding to possible outcomes of the experiments. As multiple models can be derived that adequately represent the observed phenomena, we apply Occam's razor by providing an explicit criterion for model selection based on Bayesian statistics. We then employ this criterion to select a model among competing models calibrated to experimental data for rubber and brain tissue under single or multiaxial loads.

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Section: Example 3 (Human Brain)mentioning

confidence: 99%

Section: (A) Calibration Of Random Field Parametersmentioning

confidence: 99%

Section: (A) Calibration Of Random Field Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic isotropic hyperelastic materials: constitutive calibration and model selection

2018

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“…[7][8][9][10] Hydrogels are particularly useful to mimic soft tissues. They enable in vitro culture of multiple cell types by mimicking in vivo matrix elasticity of various tissues and organs.…”

Section: Influence Of Mechanical Properties In Bioengineered Hydrogelsmentioning

confidence: 99%

Dynamic bioengineered hydrogels as scaffolds for advanced stem cell and organoid culture

2017

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Bioengineered hydrogels enable systematic variation of mechanical and biochemical properties, resulting in the identification of optimal in vitro three-dimensional culture conditions for individual cell types. As the scientific community attempts to mimic and study more complex biologic processes, hydrogel design has become multi-faceted. To mimic organ and tissue heterogeneity in terms of spatial arrangement and temporal changes, hydrogels with spatiotemporal control over mechanical and biochemical properties are needed. In this prospective article, we present studies that focus on the development of hydrogels with dynamic mechanical and biochemical properties, highlighting the discoveries made using these scaffolds.

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2023

Multiscale Biomechanics

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Mechanical characterization of human brain tissue

Cited by 443 publications

References 64 publications

Stochastic isotropic hyperelastic materials: constitutive calibration and model selection

Stochastic isotropic hyperelastic materials: constitutive calibration and model selection

Dynamic bioengineered hydrogels as scaffolds for advanced stem cell and organoid culture

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