Quantifying Morphology-Related Deviations in Brain Strain Using an Automated Mesh Morphing Method.

Area of Science:

• Biomechanics
• Neuroscience
• Computational Modeling

Background:

• Finite element head models (FEHMs) are crucial for TBI biomechanics research.
• Current FEHMs often use average head shapes, neglecting individual brain morphology.
• This omission can affect the accuracy of TBI injury prediction.

Purpose of the Study:

• To develop an automated method for creating subject-specific finite element head models (FEHMs).
• To investigate the influence of individual brain morphology on biomechanical responses during impact.
• To assess the impact of morphological variations on traumatic brain injury (TBI) risk.

Main Methods:

• Developed an automated mesh morphing technique using radial basis function-thin plate spline (RBF-TPS).
• Integrated automated landmark extraction and projection for model customization.
• Applied diverse head kinematics from six datasets to five subject-specific models and a baseline model.

Main Results:

• Morphology-related deviations in maximum principal strain (MPS95) and strain rate (MPSR95) increased with loading severity.
• TBI risk thresholds varied significantly across models (up to 19.4% for MPS95, 11.4% for MPSR95).
• Brain volume generally correlated with strain magnitudes, but exceptions showed smaller brains with higher responses, indicating size alone is insufficient.

Conclusions:

• Subject-specific brain morphology significantly influences biomechanical responses and TBI risk predictions.
• Existing FEHMs may not accurately capture injury risk due to the lack of individual morphological data.
• The developed automated morphing method and findings underscore the necessity of incorporating personalized morphology into TBI prediction models.