Development of a Second-Order System for Rapid Estimation of Maximum Brain Strain

Gabler, Lee F.; Crandall, Jeff R.; Panzer, Matthew B.

doi:10.1007/s10439-018-02179-9

Cited by 99 publications

(77 citation statements)

References 26 publications

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“…One strategy is to simplify the model and response output. For example, several reduced-order models have been proposed, including a one-degree-of-freedom (DOF) dynamic model based on modal analysis 19 or equation of motion 20 , and a second-order model 21 . By fitting parameters of a reduced-order model against directly simulated responses obtained from a finite element (FE) model of the human head, hybrid brain injury metrics were developed to correlate with peak maximum principal strain (MPS) of the whole brain.…”

Section: Introductionmentioning

confidence: 99%

“…By fitting parameters of a reduced-order model against directly simulated responses obtained from a finite element (FE) model of the human head, hybrid brain injury metrics were developed to correlate with peak maximum principal strain (MPS) of the whole brain. These metrics have shown promise over other conventional injury metrics derived solely from rotational kinematics when correlating against MPS of the whole brain over a large spectrum of impact severities and in diverse injury scenarios on a group-wise basis 21,22 .…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural network for efficient estimation of regional brain strains

Zhao

Ghazi

et al. 2019

Sci Rep

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Head injury models are important tools to study concussion biomechanics but are impractical for real-world use because they are too slow. Here, we develop a convolutional neural network (CNN) to estimate regional brain strains instantly and accurately by conceptualizing head rotational velocity profiles as two-dimensional images for input. We use two impact datasets with augmentation to investigate the CNN prediction performances with a variety of training-testing configurations. Three strain measures are considered, including maximum principal strain (MPS) of the whole brain, MPS of the corpus callosum, and fiber strain of the corpus callosum. The CNN is further tested using an independent impact dataset (N = 314) measured in American football. Based on 2592 training samples, it achieves a testing R2 of 0.916 and root mean squared error (RMSE) of 0.014 for MPS of the whole brain. Combining all impact-strain response data available (N = 3069), the CNN achieves an R2 of 0.966 and RMSE of 0.013 in a 10-fold cross-validation. This technique may enable a clinical diagnostic capability to a sophisticated head injury model, such as facilitating head impact sensors in concussion detection via a mobile device. In addition, it may transform current acceleration-based injury studies into focusing on regional brain strains. The trained CNN is publicly available along with associated code and examples at https://github.com/Jilab-biomechanics/CNN-brain-strains. They will be updated as needed in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for efficient estimation of regional brain strains

Zhao

Ghazi

et al. 2019

Sci Rep

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“…These injury metrics include HIC based on head CG linear acceleration 34 and brain injury criteria (BrIC) based on head CG rotational velocity, 45 as well as other more recent studies including diffuse axonal multi-axis general evaluation (DAMAGE). 17 Human body FE models enable the study of the localized tissue response of vital organs (e.g. strain history within the brain) during an impact, which has been correlated to injury using metrics such as cumulative strain damage measure 2,46 and the universal BrIC (UBrIC).…”

Section: Introductionmentioning

confidence: 99%

Development and Multi-Scale Validation of a Finite Element Football Helmet Model

Decker

Baker

et al. 2019

Ann Biomed Eng

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Head injury is a growing concern within contact sports, including American football. Computational tools such as finite element (FE) models provide an avenue for researchers to study, and potentially optimize safety tools, such as helmets. The goal of this study was to develop an accurate representative helmet model that could be used in further study of head injury to mitigate the toll of concussions in contact sports. An FE model of a Schutt Air XP Pro football helmet was developed through three major steps: geometry development, material characterization, and model validation. The fully assembled helmet model was fit onto a Hybrid III dummy head–neck model and National Operating Committee on Standards for Athletic Equipment (NOCSAE) head model and validated through a series of 67 representative impacts similar to those experienced by a football player. The kinematic and kinetic response of the model was compared to the response of the physical experiments, which included force, head linear acceleration, head angular velocity, and carriage acceleration. The outputs between the model and the physical tests were quantitatively evaluated using CORelation and Analysis (CORA), amounting to an overall averaged score of 0.76. The model described in this study has been extensively validated and can function as a building block for innovation in player safety.

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“…From these, the 95th percentile value was recorded, resulting in a single metric of brain deformation for each of the 129 parcellated brain regions in a given impact case. The global 95th percentile MPS (MPS95) was also calculated, considering all elements in the brain, as is commonly done in FE brain injury analysis to avoid potential numerical instabilities (Panzer et al, 2012;Beckwith et al, 2018;Gabler et al, 2019;Miller et al, 2019;Sanchez et al, 2019;Wu et al, 2019a). Betzel et al (2016) performed diffusion spectrum imaging (DSI) for a total of 30 subjects along with T1-weighted anatomical scans.…”

Section: Finite Element Modelingmentioning

confidence: 99%

“…Nearly all computational models used to estimate brain injury risk consider the maximum deformation in the brain, regardless of where it occurs, as the primary metric that correlates to injury risk (Takhounts et al, 2013;Gabler et al, 2018Gabler et al, , 2019. While these global deformation metrics are suitable for assessing the severity of head impact, they lack the relationships that link local tissue deformation to pathological or functional deficits.…”

Section: Introductionmentioning

confidence: 99%

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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Development of a Second-Order System for Rapid Estimation of Maximum Brain Strain

Cited by 99 publications

References 26 publications

Convolutional neural network for efficient estimation of regional brain strains

Convolutional neural network for efficient estimation of regional brain strains

Development and Multi-Scale Validation of a Finite Element Football Helmet Model

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

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