Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 6, 2026

Controlled Cortical Impact Model for Traumatic Brain Injury
05:30

Controlled Cortical Impact Model for Traumatic Brain Injury

Published on: August 5, 2014

29.4K

A Shape and Size-Scaled Deep Learning Brain Injury Model for Near Real-Time Dynamic Impact Simulation.

Wei Zhao1,2, Songbai Ji3,2

  • 1Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01605.

Journal of Biomechanical Engineering
|October 24, 2025
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same authorSame journal

Estimating Cell Mechanical Anisotropy via Spherical Indentation and F-actin Imaging.

Journal of biomechanical engineering·2026
Same author

Optimizing foam padding of the advanced combat helmet to maximize protection of blast-induced brain injury and wearing comfort.

Advances in engineering software (Barking, London, England : 1992)·2026
Same author

A model of axonal damage accumulation from real-world head impact exposure.

Annals of biomedical engineering·2026
Same author

Large-scale modeling of axonal dynamic responses via deep learning.

Biomechanics and modeling in mechanobiology·2025
Same author

Publisher Correction: On-field Head Acceleration Exposure Measurement Using Instrumented Mouthguards: Missing Data Imputation for Complete Exposure Analysis.

Annals of biomedical engineering·2025
Same author

High-order mesoscale modeling with geometrically conforming gray/white matter interface for traumatic brain injury.

Computer methods and programs in biomedicine·2025

This study developed an individualized deep learning model for rapid brain injury prediction, improving upon generic models for diverse populations. The enhanced model accurately estimates brain deformation and strain, crucial for understanding traumatic brain injury (TBI).

Area of Science:

  • Biomechanics
  • Computational modeling
  • Neuroscience

Background:

  • Traumatic brain injury (TBI) research requires efficient models for large-scale, strain-based investigations.
  • Previous models were limited to generic brain surrogates, hindering individualized analysis.
  • Accurate estimation of brain deformation and strain is vital for understanding TBI mechanisms.

Purpose of the Study:

  • To extend an existing deep learning model for traumatic brain injury (TBI) to an individualized surrogate applicable to males, females, and youth.
  • To enable rapid estimation of brain deformation and strain over impact duration.
  • To improve the integration of repetitive head impacts in multiscale modeling frameworks.

Main Methods:

  • Utilized a training dataset (N=1363) from a scaled anisotropic Worcester Head Injury Model (WHIM) V1.0 simulating head impacts.
Keywords:
Worcester Head Injury Modelaxonal injuryconcussionconvolutional neural networkdeep learningtraumatic brain injury

More Related Videos

Murine Model of Controlled Cortical Impact for the Induction of Traumatic Brain Injury
05:01

Murine Model of Controlled Cortical Impact for the Induction of Traumatic Brain Injury

Published on: August 16, 2019

11.0K
Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury
09:49

Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury

Published on: January 20, 2023

3.6K

Related Experiment Videos

Last Updated: Jan 6, 2026

Controlled Cortical Impact Model for Traumatic Brain Injury
05:30

Controlled Cortical Impact Model for Traumatic Brain Injury

Published on: August 5, 2014

29.4K
Murine Model of Controlled Cortical Impact for the Induction of Traumatic Brain Injury
05:01

Murine Model of Controlled Cortical Impact for the Induction of Traumatic Brain Injury

Published on: August 16, 2019

11.0K
Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury
09:49

Assessing Changes in Synaptic Plasticity Using an Awake Closed-Head Injury Model of Mild Traumatic Brain Injury

Published on: January 20, 2023

3.6K
  • Voxelized brain-skull relative displacement based on three anatomical scaling factors at 1ms temporal resolution.
  • Modified a multitask convolutional neural network (CNN) architecture with transfer learning, incorporating scaling factors as additional inputs.

    • Main Results:

    • The scaled CNN achieved high accuracy for peak displacement magnitude (R2=0.96±0.05, RMSE=0.25±0.20mm) and maximum principal strain (MPS) (R2=0.83±0.16, RMSE=0.02±0.01).
    • Achieved high efficiency, with predictions in <1s compared to >30min for direct simulations.
    • Demonstrated success rates of 81.0% for displacement and 70.8% for MPS at peak impact.

    Conclusions:

    • The developed individualized deep learning model enables efficient and rapid estimation of brain deformation and strain.
    • This approach facilitates more personalized modeling of head impacts and axonal injury.
    • The work addresses a critical gap in multiscale modeling for white matter injury research.