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Exploring hyperelastic material model discovery for human brain cortex: Multivariate analysis vs. artificial neural

Jixin Hou1, Nicholas Filla1, Xianyan Chen2

  • 1School of ECAM, College of Engineering, University of Georgia, Athens, GA, 30602, USA.

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Summary

Artificial neural networks can identify accurate brain tissue models, but simplified multiple regression models offer superior predictive accuracy. Rigorous validation is key for optimal constitutive model selection in biomechanics.

Keywords:
Brain mechanicsConstitutive artificial neural networksConstitutive modelingHuman brain cortexMultiple regression

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Area of Science:

  • Biomechanics
  • Computational Biology
  • Materials Science

Background:

  • The human brain's complex mechanical properties are crucial for its function.
  • Accurate constitutive models are needed to predict brain tissue mechanics.
  • Traditional methods like finite element analysis require effective material models.

Purpose of the Study:

  • To identify suitable material models for human brain tissue.
  • To compare artificial neural networks (ANNs) and multiple regression for constitutive model discovery.
  • To evaluate model efficacy and predictive accuracy.

Main Methods:

  • Applied ANNs and multiple regression to classic constitutive models.
  • Systematically compared outcomes from both techniques.
  • Employed strategies to mitigate overfitting and ensure rigorous cross-validation.

Main Results:

  • ANNs successfully identified accurate constitutive models.
  • Simplified multiple regression models (two-term and single-term) outperformed ANN models.
  • ANN models trained under specific loading scenarios were suboptimal.
  • Multiple regression with information criteria proved highly effective.

Conclusions:

  • Both ANNs and multiple regression can discover optimal constitutive models for brain tissue.
  • Simplified regression models offer enhanced predictive accuracy.
  • Emphasizes the need for rigorous validation of regularization parameters in ANNs.
  • Highlights the utility of traditional regression for complex biological tissue modeling.