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Homogenization of heterogeneous brain tissue under quasi-static loading: a visco-hyperelastic model of a 3D RVE

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Abstract

Researches, in the recent years, reveal the utmost importance of brain tissue assessment regarding its mechanical properties, especially for automatic robotic tools, surgical robots and helmet producing. For this reason, experimental and computational investigation of the brain behavior under different conditions seems crucial. However, experiments do not normally show the distribution of stress and injury in microscopic scale, and due to various factors are costly. Development of micromechanical methods, which could predict the brain behavior more appropriately, could highly be helpful in reducing these costs. This study presents computational analysis of heterogeneous part of the brain tissue under quasi-static loading. Heterogeneity is created by irregular distribution of neurons in a representative volume element (RVE). Considering time-dependent behavior of the tissue, a visco-hyperelastic constitutive model is developed to predict the RVE behavior more realistically. The RVE is studied in different loads and load rates; 1, 2, 3, 10 and 15% strain load are applied at 0.03 and 0.2 s on the RVE as tensile and shear loads. Due to complexity in geometry, self-consistent approximation method is employed to increase the volume fraction of neurons and analyze RVE behavior in various NVFs. The results show increasing the load rate leads to a raise in the maximum stress that indicates the tissue is more vulnerable at higher rates. Moreover, stiffness of the tissue is enhanced in higher NVFs. Additionally, it is found that axons undergo higher stresses; hence, they are more sensitive in accidents which lead to axonal death and would cause TBI and DAI.

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References

  • Abdel Rahman R et al (2012) An asymptotic method for the prediction of the anisotropic effective elastic properties of the cortical vein: superior sagittal sinus junction embedded within a homogenized cell element. Journal of Mechanics of Materials and Structures 7(6):593–611

    Article  Google Scholar 

  • Abolfathi N et al (2009) A micromechanical procedure for modelling the anisotropic mechanical properties of brain white matter. Computer Methods in Biomechanics and Biomedical Engineering 12(3):249–262

    Article  Google Scholar 

  • Bergström J, Boyce M (1998) Constitutive modeling of the large strain time-dependent behavior of elastomers. J Mech Phys Solids 46(5):931–954

    Article  MATH  Google Scholar 

  • Bernick KB et al (2011) Biomechanics of single cortical neurons. Acta Biomater 7(3):1210–1219

    Article  Google Scholar 

  • Budday S et al (2015) Mechanical properties of gray and white matter brain tissue by indentation. J Mech Behav Biomed Mater 46:318–330

    Article  Google Scholar 

  • Christ AF et al (2010) Mechanical difference between white and gray matter in the rat cerebellum measured by scanning force microscopy. J Biomech 43(15):2986–2992

    Article  Google Scholar 

  • Cloots RJ et al (2013) Multi-scale mechanics of traumatic brain injury: predicting axonal strains from head loads. Biomech Model Mechanobiol 12(1):137–150

    Article  Google Scholar 

  • Coudrillier B et al (2013) Scleral anisotropy and its effects on the mechanical response of the optic nerve head. Biomech Model Mechanobiol 12(5):941–963

    Article  Google Scholar 

  • Couper Z, Albermani F (2008) Infant brain subjected to oscillatory loading: material differentiation, properties, and interface conditions. Biomech Model Mechanobiol 7(2):105

    Article  Google Scholar 

  • Dréo J et al (2006) Metaheuristics for hard optimization: methods and case studies. Springer, Berlin

    MATH  Google Scholar 

  • Faul M et al (2010) Traumatic brain injury in the United States: national estimates of prevalence and incidence, 2002–2006. Injury Prevention 16(Suppl 1):A268

    Article  Google Scholar 

  • Feng Y et al (2013) Measurements of mechanical anisotropy in brain tissue and implications for transversely isotropic material models of white matter. J Mech Behav Biomed Mater 23:117–132

    Article  Google Scholar 

  • Forte AE, Galvan S, Dini D (2018) Models and tissue mimics for brain shift simulations. Biomech Model Mechanobiol 17(1):249–261

    Article  Google Scholar 

  • Ganpule S et al (2013) Mechanics of blast loading on the head models in the study of traumatic brain injury using experimental and computational approaches. Biomech Model Mechanobiol 12(3):511–531

    Article  Google Scholar 

  • Hashin Z (1983) Analysis of composite materials—a survey. J Appl Mech 50(3):481–505

    Article  MATH  Google Scholar 

  • Hiscox LV et al (2016) Magnetic resonance elastography (MRE) of the human brain: technique, findings and clinical applications. Phys Med Biol 61(24):R401

    Article  Google Scholar 

  • Holzapfel GA (2000) Nonlinear solid mechanics: a continuum approach for engineering, 1st edn. Wiley, New York

    MATH  Google Scholar 

  • Horgan CO, Murphy JG (2009) On the volumetric part of strain-energy functions used in the constitutive modeling of slightly compressible solid rubbers. Int J Solids Struct 46(16):3078–3085

    Article  MATH  Google Scholar 

  • Javid S, Rezaei A, Karami G (2014) A micromechanical procedure for viscoelastic characterization of the axons and ECM of the brainstem. J Mech Behav Biomed Mater 30:290–299

    Article  Google Scholar 

  • Ji S et al (2014) Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation. Biomech Model Mechanobiol 13(5):1121–1136

    Article  Google Scholar 

  • Kanit T et al (2003) Determination of the size of the representative volume element for random composites: statistical and numerical approach. Int J Solids Struct 40(13):3647–3679

    Article  MATH  Google Scholar 

  • Karami G et al (2009) A micromechanical hyperelastic modeling of brain white matter under large deformation. J Mech Behav Biomed Mater 2(3):243–254

    Article  Google Scholar 

  • Karami G, Shankar S, Ziejewski M, Azarmi F (2010) Micromechanical hyperelastic modeling of bi-directional oriented axons in brain white matter. In: Proc. ASME. biomedical and biotechnology engineering, pp 619–625

  • Kleiven S, von Holst H (2002) Consequences of head size following trauma to the human head. J Biomech 35(2):153–160

    Article  Google Scholar 

  • Koser DE et al (2015) CNS cell distribution and axon orientation determine local spinal cord mechanical properties. Biophys J 108(9):2137–2147

    Article  Google Scholar 

  • Kyriacou SK et al (2002) Brain mechanics for neurosurgery: modeling issues. Biomech Model Mechanobiol 1(2):151–164

    Article  Google Scholar 

  • Labus KM, Puttlitz CM (2016) An anisotropic hyperelastic constitutive model of brain white matter in biaxial tension and structural–mechanical relationships. J Mech Behav Biomed Mater 62:195–208

    Article  Google Scholar 

  • Laksari K, Shafieian M, Darvish K (2012) Constitutive model for brain tissue under finite compression. J Biomech 45(4):642–646

    Article  Google Scholar 

  • Laksari K et al (2015) Computational simulation of the mechanical response of brain tissue under blast loading. Biomech Model Mechanobiol 14(3):459–472

    Article  Google Scholar 

  • Lee S et al (2014) Measurement of viscoelastic properties in multiple anatomical regions of acute rat brain tissue slices. J Mech Behav Biomed Mater 29:213–224

    Article  Google Scholar 

  • Libertiaux V, Pascon F, Cescotto S (2011) Experimental verification of brain tissue incompressibility using digital image correlation. J Mech Behav Biomed Mater 4(7):1177–1185

    Article  Google Scholar 

  • Maltese MR, Margulies SS (2016) Biofidelic white matter heterogeneity decreases computational model predictions of white matter strains during rapid head rotations. Computer methods in biomechanics and biomedical engineering 19:1–12

    Article  Google Scholar 

  • Menon DK et al (2010) Position statement: definition of traumatic brain injury. Arch Phys Med Rehabil 91(11):1637–1640

    Article  Google Scholar 

  • Miller K (1999) Constitutive model of brain tissue suitable for finite element analysis of surgical procedures. J Biomech 32(5):531–537

    Article  Google Scholar 

  • Mura T (2013) Micromechanics of defects in solids. Springer, Berlin

    Google Scholar 

  • Pan Y et al (2013) Finite element modeling of CNS white matter kinematics: use of a 3D RVE to determine material properties. Frontiers in bioengineering and biotechnology 1:19

    Article  Google Scholar 

  • Pervin F, Chen WW (2009) Dynamic mechanical response of bovine gray matter and white matter brain tissues under compression. J Biomech 42(6):731–735

    Article  Google Scholar 

  • Peter SJ, Mofrad MR (2012) Computational modeling of axonal microtubule bundles under tension. Biophys J 102(4):749–757

    Article  Google Scholar 

  • Poli R (2007) An analysis of publications on particle swarm optimization applications. Department of Computer Science, University of Essex, Essex

    Google Scholar 

  • Rashid B, Destrade M, Gilchrist MD (2014) Mechanical characterization of brain tissue in tension at dynamic strain rates. J Mech Behav Biomed Mater 33:43–54

    Article  Google Scholar 

  • Rémond Y et al (2016) Homogenization of reconstructed RVE. In: Rémond Y, Ahzi S, Baniassadi M, Garmestani H (eds) Applied RVE reconstruction and homogenization of heterogeneous materials. Wiley, Hoboken

    Chapter  Google Scholar 

  • Schmid-Schönbein G et al (1981) Passive mechanical properties of human leukocytes. Biophys J 36(1):243–256

    Article  Google Scholar 

  • Shaoning S (2014) Mechanical characterization and modeling of polymer/clay nanocomposites

  • Sheidaei A et al (2013) 3-D microstructure reconstruction of polymer nano-composite using FIB–SEM and statistical correlation function. Composites Science and Technology 80:47–54

    Article  Google Scholar 

  • Shulyakov AV et al (2009) Simultaneous determination of mechanical properties and physiologic parameters in living rat brain. Biomech Model Mechanobiol 8(5):415–425

    Article  Google Scholar 

  • Sotudeh-Chafi M et al (2008) A multi-scale finite element model for shock wave-induced axonal brain injury. In: ASME 2008 summer bioengineering conference. American Society of Mechanical Engineers

  • Tanielian T et al (2008) Invisible wounds of war. Summary and recommendations for addressing psychological and cognitive injuries. RAND Corp, Santa Monica

    Book  Google Scholar 

  • Torquato S (2013) Random heterogeneous materials: microstructure and macroscopic properties, vol 16. Springer, Berlin

    MATH  Google Scholar 

  • Vappou J et al (2007) Magnetic resonance elastography compared with rotational rheometry for in vitro brain tissue viscoelasticity measurement. Magn Reson Mater Phys, Biol Med 20(5–6):273

    Article  Google Scholar 

  • Wang HC, Wineman AS (1972) A mathematical model for the determination of viscoelastic behavior of brain in vivo—I Oscillatory response. J Biomech 5(5):431–446

    Article  Google Scholar 

  • Whitford C et al (2018) A viscoelastic anisotropic hyperelastic constitutive model of the human cornea. Biomech Model Mechanobiol 17(1):19–29

    Article  Google Scholar 

  • Yousefi E et al (2017) Effect of nanofiller geometry on the energy absorption capability of coiled carbon nanotube composite material. Composites Science and Technology 153:222–231

    Article  Google Scholar 

  • Zhang M-G et al (2014) Spherical indentation method for determining the constitutive parameters of hyperelastic soft materials. Biomech Model Mechanobiol 13(1):1–11

    Article  MathSciNet  Google Scholar 

Download references

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Correspondence to Mostafa Baghani.

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Kazempour, M., Baniassadi, M., Shahsavari, H. et al. Homogenization of heterogeneous brain tissue under quasi-static loading: a visco-hyperelastic model of a 3D RVE. Biomech Model Mechanobiol 18, 969–981 (2019). https://doi.org/10.1007/s10237-019-01124-6

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