Accuracy meets simplicity: A constitutive model for heterogenous brain tissue

Area of Science:

• Biomechanics
• Materials Science
• Neuroscience

Background:

• Understanding brain tissue mechanics is crucial for diagnosing and treating neurological conditions.
• Existing models often struggle to accurately represent the complex hyperelastic behavior of brain tissue.

Purpose of the Study:

• To introduce a novel, general, hyperelastic, stretch-based potential for modeling brain tissue.
• To develop and validate a specific four-parameter model derived from this potential.

Main Methods:

• Developed a general hyperelastic, stretch-based potential.
• Derived a specific four-parameter model from the general potential.
• Validated the model against experimental data for various brain regions (cortex, basal ganglia, corona radiata, corpus callosum) under tension, compression, and shear.

Main Results:

• The proposed four-parameter model demonstrated superior performance compared to modified Ogden, Gent, Demiray, and machine-learning models.
• Achieved high R-squared values (0.896-0.997) across different brain regions and loading conditions (tension, compression, shear).
• The model accurately captured brain tissue elasticity.

Conclusions:

• The developed stretch-based model provides a promising and accurate approach for simulating brain tissue mechanics.
• This model can enhance finite element models for investigating brain development, injuries, and diseases.
• Offers a valuable tool for advancing the understanding of neuro-mechanics.