Modelling the rate-dependent mechanical behaviour of the brain tissue.

Area of Science:

• Biomechanics
• Materials Science
• Neuroscience

Background:

• Understanding the rate-dependent mechanical behavior of brain tissue is crucial for injury prediction and treatment.
• Existing models may lack simplicity, versatility, or predictive accuracy across various deformation rates.

Purpose of the Study:

• To introduce and validate a novel, simple, and versatile modeling approach for brain tissue's rate-dependent mechanical behavior.
• To assess the model's accuracy in capturing mechanical responses under diverse loading conditions and rates.

Main Methods:

• A new modeling approach was developed for incompressible isotropic brain tissue.
• The model was applied to existing datasets covering compression, tension, and shear deformations.
• Deformation rates ranged from quasi-static to blast-loading conditions (~1000 s⁻¹).

Main Results:

• The proposed model demonstrated favorable accuracy in capturing rate-dependent behaviors.
• The model successfully fitted single- and multi-mode deformations across all tested rates.
• Model parameters were calibrated using quasi-static data, with predictions validated at other rates.

Conclusions:

• The developed modeling framework offers a simple, versatile, and accurate method for analyzing brain tissue mechanics.
• This approach shows significant potential for applications in neurotrauma research and protective device design.