Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels

Area of Science:

• Biomedical Engineering
• Computational Mechanics
• Materials Science

Background:

• Accurate material models are crucial for understanding brain tissue mechanics.
• A nonlinear poro-viscoelastic model based on the Theory of Porous Media was recently developed.
• This model accounts for time-dependent behavior from solid matrix viscoelasticity and fluid interaction.

Purpose of the Study:

• To characterize the parameters of a novel nonlinear poro-viscoelastic computational model.
• To validate the model's ability to represent brain tissue mechanical properties.
• To extract optimal constitutive model parameters for brain tissue.

Main Methods:

• Indentation experiments were performed on a polyvinyl alcohol hydrogel mimicking brain tissue.
• An inverse parameter identification scheme with a trust region reflective algorithm was employed.
• Finite element simulations were used to match experimental data and extract model parameters.

Main Results:

• The study successfully characterized the parameters of the poro-viscoelastic model for brain tissue.
• The model, with extracted parameters, accurately represented the mechanical behavior of the hydrogel.
• Validation through finite element simulation confirmed the model's efficacy.

Conclusions:

• The developed computational model and parameter identification method are effective for characterizing brain tissue.
• This approach provides a robust framework for calibrating material models used in brain tissue research.
• The findings contribute to the development of more reliable computational tools for neuroscience and biomechanics.