Biomechanics and Modeling in Mechanobiology

, Volume 18, Issue 6, pp 1927–1945 | Cite as

Determining constitutive behavior of the brain tissue using digital image correlation and finite element modeling

  • Amir Mohammad Felfelian
  • Amirhosein Baradaran Najar
  • Reza Jafari NedoushanEmail author
  • Hossein Salehi
Original Paper


Detailed knowledge about the mechanical properties of brain can improve numerical modeling of the brain under various loading conditions. The success of this modeling depends on constitutive model and reliable extraction of its material constants. The isotropy of the brain tissue is a key factor which affects the form of constitutive models. In this study, compression tests were performed on different parts of the sheep brain tissue. Also, the digital image correlation (DIC) method was utilized to investigate the direction dependency of brain parts considering their microstructures. To this aim, the DIC method was employed to measure the transverse strain of two lateral sides of the tissue samples. The results of DIC method revealed that the brain stem and corona radiata were isotropic, while the mixed white and gray matter showed an unrepeatable behavior depending on the extracted sample. To examine and validate DIC method, stress–strain diagrams were also used to investigate the isotropy. It could be concluded that axonal fibers had no reinforcing role in the brain tissue. Furthermore, the DIC method indicated incompressibility of the brain tissue. Then, the significance of using a correct method to extract the material constants of brain was discussed. In other words, the effect of the real boundary conditions in experiments, which was neglected in most previous studies, was taken into account here. Finally, the particle swarm optimization algorithm along with the finite element modeling was used to estimate the hyper-viscoelastic constants of different parts of the brain tissue.


Brain tissue Mechanical characterization Reinforcing role of axons Digital image correlation Finite element modeling Particle swarm optimization 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Amir Mohammad Felfelian
    • 1
  • Amirhosein Baradaran Najar
    • 1
  • Reza Jafari Nedoushan
    • 1
    Email author
  • Hossein Salehi
    • 2
  1. 1.Department of Mechanical EngineeringIsfahan University of TechnologyIsfahanIran
  2. 2.Department of Anatomical Sciences, School of MedicineIsfahan University of Medical SciencesIsfahanIran

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