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Updated: Dec 22, 2025

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
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Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis.

Erin D Anderson1, J Sebastian Giudice2, Taotao Wu2

  • 1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States.

Frontiers in Bioengineering and Biotechnology
|May 1, 2020
PubMed
Summary

Reducing concussion risk requires understanding individual brain networks. This study integrates brain deformation models with network analysis, finding that individual brain connectivity significantly impacts concussion prediction accuracy.

Keywords:
biomechanicsconcussiongraph theorynetworksstructural connectivity

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Area of Science:

  • Neuroscience
  • Biomechanics
  • Computational Biology

Background:

  • Concussion affects millions annually, necessitating improved injury prevention.
  • Current finite element (FE) models lack individual brain connectivity data for concussion risk assessment.
  • Understanding brain connectivity's role in concussion is crucial for developing better protective equipment.

Purpose of the Study:

  • To integrate FE-predicted brain deformations with graph theory network analysis.
  • To identify brain regions critical for network communication and their relation to concussion risk.
  • To investigate the influence of individual brain structural connectivities on concussion prediction.

Main Methods:

  • Computed maximum principal strain in 129 brain regions from 53 impact reconstructions.
  • Simulated structural lesioning in 129 brain regions using diffusion spectrum imaging data from 30 subjects.
  • Assessed changes in global network efficiency after simulated regional removal.

Main Results:

  • High-strain regions did not overlap with regions critical for network communication (ρ = 0.07, p = 0.45).
  • Concussion prediction accuracy was similar using either high-strain or high-efficiency regions.
  • Concussion prediction accuracy varied significantly across individual brain connectomes.

Conclusions:

  • Individual brain network structure is a significant factor in concussion prediction.
  • Network efficiency analysis provides insights comparable to strain analysis for concussion prediction.
  • Further research into individual connectomes may enhance concussion prediction and protective equipment design.