ArticleBiomechanics and modeling in mechanobiology2020
Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury.
Article in Biomechanics and modeling in mechanobiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 85 citations in OpenAlex.
- Mechanical deformation explains distinct neuroimaging patterns and etiologies in brain trauma.NeuroImage · 2026Article
- Mechanisms of inflammatory response in secondary brain injury: a review of computational approaches, challenges, and future directions.Biomechanics and modeling in mechanobiology · 2026Review
- A Model of Traumatic Brain Injury Oligomerizes Tau in Cortical Organoids and Induces Clinically Relevant Pathologies that Synergize withJournal of neurotrauma · 2025Article
- Relationship Between Rotational Device Kinematics and Head Kinematics in a Large Animal Model of Traumatic Brain Injury.Annals of biomedical engineering · 2025Article
- Exploring Sex-Based Variations in Head Kinematics During Soccer Heading.Annals of biomedical engineering · 2025Article
- Peaks and Distributions of White Matter Tract-related Strains in Bicycle Helmeted Impacts: Implication for Helmet Ranking and Optimization.Annals of biomedical engineering · 2025Article
- Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement.Communications engineering · 2025Article
- Quantitative video analysis of head acceleration events: a review.Frontiers in bioengineering and biotechnology · 2025Review
- Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks.IEEE sensors journal · 2024Article
- Position-based assessment of head impact frequency, severity, type, and location in high school American football.Frontiers in bioengineering and biotechnology · 2024Article
- Developing a porcine model of severe traumatic brain injury induced by high amplitude rotational acceleration.Brain & spine · 2024Article
- Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.Journal of sport and health science · 2023Article
- Pupillary Light Response Deficits in 4-Week-Old Piglets and Adolescent Children after Low-Velocity Head Rotations and Sports-Related Concussions.Biomedicines · 2023Article
- Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports.Annals of biomedical engineering · 2022Review
- Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.IEEE transactions on bio-medical engineering · 2022Article
- Brain architecture-based vulnerability to traumatic injury.Frontiers in bioengineering and biotechnology · 2022Article
- The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts.Frontiers in bioengineering and biotechnology · 2022Article
- Injury Metrics for Assessing the Risk of Acute Subdural Hematoma in Traumatic Events.International journal of environmental research and public health · 2021Article
- Biofidelic dynamic compression of human cortical spheroids reproduces neurotrauma phenotypes.Disease models & mechanisms · 2021Article
- Brain aging mechanisms with mechanical manifestations.Mechanisms of ageing and development · 2021Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI). All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73-90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.