Fitted hyperelastic parameters for Human brain tissue from reported tension, compression, and shear tests

Area of Science:

• Biomechanics
• Biomaterials Science
• Computational Mechanics

Background:

• Understanding human brain tissue mechanical properties is vital for brain trauma research and developing treatments.
• Existing hyperelastic models often use parameters fitted to single loading types (tension, compression, or shear).
• Previous studies reported experimental data but lacked readily usable parameters for finite element simulations.

Purpose of the Study:

• To determine optimal hyperelastic model parameters for human brain tissue under quasi-static loading.
• To simultaneously fit parameters using experimental data from tension, compression, and shear loading modes.
• To provide energy function parameters suitable for finite element analysis (FEA) in ABAQUS.

Main Methods:

• Utilized ex vivo human brain tissue data from Jin et al. (2013) encompassing three loading modes and multiple brain regions.
• Employed an optimization process in MATLAB, iteratively calling ABAQUS FEA models for tension, compression, and shear.
• Fitted parameters for hyperfoam, Ogden, and polynomial strain energy functions using low strain rate experimental data.

Main Results:

• Achieved a relatively good fit to experimental data across all three loading modes using two-term energy functions.
• Determined shear modulus values within the range of 897-1653 Pa, consistent with other published studies.
• Generated energy function parameters applicable to brain tissue simulations in FEA.

Conclusions:

• The study successfully identified robust hyperelastic parameters for human brain tissue modeling.
• The derived parameters enable more accurate quasi-static simulations of brain tissue behavior under various loads.
• These findings contribute essential data for advancing computational models in neurotrauma research and surgical planning.