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Mesoscale Simulation-Based Parametric Study of Damage Potential in Brain Tissue Using Hyperelastic and Internal State

Ge He1, Lei Fan2, Yucheng Liu3

  • 1Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai 200444, China.

Journal of Biomechanical Engineering
|December 13, 2021
PubMed
Summary

This study used finite element analysis to investigate brain tissue damage. Gray matter thickness and humidity significantly impact stress triaxiality, while brain region influences strain in sulci.

Keywords:
brain tissuedesign of experimentshyperelasticityinternal state variable

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Area of Science:

  • Biomechanics
  • Computational Neuroscience
  • Materials Science

Background:

  • Brain tissue exhibits complex mechanical behaviors influenced by hydration and morphology.
  • Understanding stress and strain distribution is crucial for modeling traumatic brain injury (TBI).

Purpose of the Study:

  • To perform a two-dimensional mesoscale finite element analysis of multilayered brain tissue.
  • To calculate damage-related average stress triaxiality and local maximum von Mises strain.
  • To screen the influence of morphological and environmental parameters on brain tissue damage.

Main Methods:

  • Mesoscale finite element analysis (FEA) of multilayered brain tissue.
  • Integration of rate-dependent hyperelastic and internal state variable (ISV) models for wet and dry tissues.
  • Statistical design of experiments (DOE) to screen seven parameters (morphology, strain rate, humidity).

Main Results:

  • Gray matter thickness and humidity were the most critical parameters for average stress triaxiality.
  • Brain lobe/region was the most influential factor for local maximum von Mises strain at sulcal depths.
  • FEA results provide insights into damage growth and coalescence.

Conclusions:

  • Identified key parameters influencing brain tissue mechanical response under load.
  • Results inform the development of refined macroscale brain damage models.
  • Highlights the importance of considering tissue properties and environmental conditions in injury prediction.