ArticleFrontiers in bioengineering and biotechnology2020
Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 34 citations in OpenAlex.
- Mechanical deformation explains distinct neuroimaging patterns and etiologies in brain trauma.NeuroImage · 2026Article
- A Proposed Novel Neurovascular Mechanism for Brain Network Dysfunction After Traumatic Injury.Annals of biomedical engineering · 2026Article
- Axonal tract-integrated finite element brain model for predicting mild traumatic brain injury based on axonal strain.Frontiers in bioengineering and biotechnology · 2026Article
- Development of a Detailed Finite Element Model of the BIPED MK2 and Verification of Fidelity in Two Cases of Blunt Impact.Annals of biomedical engineering · 2025Article
- A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations.Communications engineering · 2025Article
- Individualized mouse brain network models produce asymmetric patterns of functional connectivity after simulated traumatic injury.Network neuroscience (Cambridge, Mass.) · 2025Article
- The biomechanical signature of loss of consciousness: computational modelling of elite athlete head injuries.Brain : a journal of neurology · 2023Review
- Biomechanics of Traumatic Head and Neck Injuries on Women: A State-of-the-Art Review and Future Directions.Biology · 2023Review
- Concussion Prone Scenarios: A Multi-Dimensional Exploration in Impact Directions, Brain Morphology, and Network Architectures Using Computational Models.Annals of biomedical engineering · 2022Article
- American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism.Annals of biomedical engineering · 2022Article
- Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports.Annals of biomedical engineering · 2022Review
- Article
- Individual-specific networks for prediction modelling - A scoping review of methods.BMC medical research methodology · 2022Article
- Brain architecture-based vulnerability to traumatic injury.Frontiers in bioengineering and biotechnology · 2022Article
- Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact.Journal of neurotrauma · 2021Article
- A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.Frontiers in bioengineering and biotechnology · 2021Article
- Influence of Anisotropic White Matter on Electroosmotic Flow Induced by Direct Current.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07;
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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.