A reanalysis of football impact reconstructions for head kinematics and finite element modeling

Sanchez, Erin J.; Gabler, Lee F.; Good, Ann Bailey; Funk, James R.; Crandall, Jeff R.; Panzer, Matthew B.

doi:10.1016/j.clinbiomech.2018.02.019

Cited by 83 publications

(59 citation statements)

References 29 publications

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“…1(a); see also the Supplemental Material [28] ]. We analyze the deformation of the brain under a range of coronal rotational accelerations that contain kinematics shown to cause concussive injury [29,30]. We choose the coronal plane since it has been shown that rotations in this direction can cause large strains in the CC [12,31], which is one of the structures often used to predict mTBI [8,32,33].…”

Section: Methodsmentioning

confidence: 99%

Nonlinear Dynamical Behavior of the Deep White Matter during Head Impact

et al. 2019

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Traumatic brain injury (TBI) is a major public health concern, affecting as many as 3 million people each year in the U.S. Despite substantial research efforts in recent years, our physical understanding of the cause of injury remains rather limited. In this paper, we characterize the nonlinear dynamical behavior of the brain-skull system through modal analysis and advanced finite-element (FE) simulations. We observe nonlinear behavior in the deep-white-matter (WM) structures near the dural folds, with an energy redistribution of around 30% between the dominant modes. We find evidence of shear-wave redirection near the falx and the tentorium (approximately 15 • in the axial and 8 • in the coronal plane) as a result of geometric nonlinearities. The shift in the falx mode shape, which is perpendicular to the deformation of the brain, causes geometrical nonlinear effects at the falx-brain tissue boundary. This is accompanied by a lateral sliding of the tentorium below the brain tissue, which induces higher local strains at its interface with deep regions of the brain. We observe that deep regions of the brain with high principal strains coincide with the identified nonlinear regions. The onset of nonlinear behavior in brain tissue is closely associated with the previously reported concussion thresholds, suggesting a possible link between the damage mechanism and the underlying nonlinear brain biomechanics.

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Section: Methodsmentioning

confidence: 99%

Nonlinear Dynamical Behavior of the Deep White Matter during Head Impact

et al. 2019

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“…The prescribed six degree-of-freedom head kinematics were FIGURE 1 | Overview of methods. (A) Sanchez et al estimated the 6 degree-of-freedom kinematics of 53 NFL impact reconstructions and then used them as inputs into a three-dimensional finite element (FE) model developed by Wu et al (2019b) to estimate the regional maximum principal strain (rMPS) in each of 129 brain regions (Sanchez et al, 2019). Image taken from Sanchez et al (2019) with permission.…”

Section: Finite Element Modelingmentioning

confidence: 99%

“…(A) Sanchez et al estimated the 6 degree-of-freedom kinematics of 53 NFL impact reconstructions and then used them as inputs into a three-dimensional finite element (FE) model developed by Wu et al (2019b) to estimate the regional maximum principal strain (rMPS) in each of 129 brain regions (Sanchez et al, 2019). Image taken from Sanchez et al (2019) with permission. (B) Betzel et al parcellated diffusion spectrum imaging from 30 healthy subjects to construct an adjacency matrix representing the structural connectivity between 129 brain regions (Betzel et al, 2016).…”

Section: Finite Element Modelingmentioning

confidence: 99%

“…originally reconstructed from helmet-to-helmet impact events occurring in professional football (Newman et al, 1999(Newman et al, , 2000(Newman et al, , 2005Pellman et al, 2003), and recently corrected by Sanchez et al (2019). More information regarding the prescribed head kinematics can be found in Figure S1.…”

Section: Finite Element Modelingmentioning

confidence: 99%

“…We hypothesize that relating an impact to the changes in global efficiency of a brain network will improve the accuracy of concussion prediction relative to methods that rely only on the maximum deformation that occurs anywhere in the brain during impact. We use reconstructed kinematic loading conditions from head impacts experienced by professional football players (Sanchez et al, 2019) to determine regions of high strain. We then simulate lesions to the structural connectivity of healthy subjects to evaluate which regions were critical for maintaining global efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Anderson

Giudice

et al. 2020

Front. Bioeng. Biotechnol.

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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; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

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Modelling of the Brain for Injury Simulation and Prevention

Yang

Mao

2019

Biomechanics of the Brain

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A reanalysis of football impact reconstructions for head kinematics and finite element modeling

Cited by 83 publications

References 29 publications

Nonlinear Dynamical Behavior of the Deep White Matter during Head Impact

Nonlinear Dynamical Behavior of the Deep White Matter during Head Impact

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Modelling of the Brain for Injury Simulation and Prevention

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