Concussion and the severity of head impacts in mixed martial arts

Tiernan, Stephen; Meagher, Aidan; O’Sullivan, David; O’Keeffe, Eoin; Kelly, Eoin; Wallace, Eugene; Doherty, Colin P.; Campbell, Matthew; Liu, Yuzhe; Domel, August G.

doi:10.1177/0954411920947850

Cited by 32 publications

(17 citation statements)

References 52 publications

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“…The remaining 53 were reconstructed head impacts in National Football League [26] using hybrid III ATD headform. The data from these two sources were merged because they both represented the responses of hybrid III ATD headform under football-type impacts; (ii) dataset 2 contained 302 on-site college football head impacts collected by the Stanford instrumented mouthguards [27][28][29]; (iii) dataset 3 contained 457 on-site mixed martial arts (MMA) head impacts collected by the Stanford instrumented mouthguard [3,30]; (iv) dataset 4 contained 48 head impacts in automobile crashworthiness tests from the National Highway Traffic Safety Administration (NHTSA) [31]; (v) dataset 5 contained 272 numerically reconstructed head impacts in National Association for Stock Car Auto Racing (NASCAR) by hybrid III ATD headform. Since finite-element (FE) modelling is the state-of-the-art biomechanics modelling tool for brain strain in head impacts, the validated KTH model [32] was used to calculate the MPS95, MPSCC95 and CSDM since these are widely used in mTBI research [6,7,23,33,34].…”

Section: Data Description and Processingmentioning

confidence: 99%

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

Zhan

Liu

et al. 2021

J. R. Soc. Interface.

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Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

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Section: Data Description and Processingmentioning

confidence: 99%

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

Zhan

Liu

et al. 2021

J. R. Soc. Interface.

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“…To prepare a dataset consisting of a variety of head impacts from different sources, we collected the head kinematics from a total of 13,623 head impacts from various sources: 1) 12,780 laboratory-reconstructed head impacts (labeled as HM for head model) simulated from a validated finite element (FE) model of the Hybrid III anthropomorphic test device (ATD) headform [31]; 2) 302 college football (labeled as CF) head impacts which were recorded by the Stanford instrumented mouthguard [20], [32]; 3) 457 MMA head impacts (labeled as MMA) which were recorded by the Stanford instrumented mouthguard [6], [33]; 4) 36 selected reconstructed helmeted head impacts from the National Football League (labeled as NFL) [34]; 5) 48 head impacts from automobile crashworthiness tests conducted by the National Highway Traffic Safety Administration (labeled as NHTSA). The distributions of the MPS, MPSR, peak resultant angular acceleration and peak resultant angular velocity are shown in Fig.…”

Section: A Data Descriptionmentioning

confidence: 99%

Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion

Zhan,

Liu,

Cecchi

et al. 2021

Preprint

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Brain strain and strain rate are effective in predicting traumatic brain injury (TBI) caused by head impacts. However, state-of-the-art finite element modeling (FEM) demands considerable computational time in the computation, limiting its application in real-time TBI risk monitoring. To accelerate, machine learning head models (MLHMs) were developed, and the model accuracy was found to decrease when the training/test datasets were from different head impacts types. However, the size of dataset for specific impact types may not be enough for model training. To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). We trained and tested the MLHMs on 13,623 head impacts from simulations, American football, mixed martial arts, car crash, and compared against the models trained on only simulations or only on-field impacts. The MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than 7s −1 in predicting MPSR on all impact datasets. The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. Besides the clinical applications in real-time brain strain and strain rate monitoring, this model helps researchers estimate the brain strain and strain rate caused by head impacts more efficiently than FEM.

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“…To explore the predictive factors in the prediction of MPS95 and find commonality and dissimilarities across various types of head impacts, the kinematics of head impacts from different sources were used: 2130 simulated head impacts from a validated finite element analysis (FEA) model of the Hybrid III anthropomorphic test dummy headform [17,9] (dataset HM), 184 college football head impacts collected by the original version of the Stanford instrumented mouthguard [18] (dataset CF1), 118 college football head impacts collected by the updated version of the Stanford instrumented mouthguard [19] (dataset CF2), 457 mixed martial arts (MMA) collected by the updated version of the Stanford instrumented mouthguard [3,20] (dataset MMA), 53 reconstructed head impacts by the National Football League (NFL) [21] (dataset NFL), 48 car crash dummy head impacts from NHTSA [22] (dataset NHTSA), and 272 numerically reconstructed head impacts in National Association for Stock Car Auto Racing (NASCAR) by Hybrid III ATD headform (dataset NASCAR).…”

Section: Data Descriptionmentioning

confidence: 99%

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

Zhan

Liu

et al. 2021

Preprint

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Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of accelerational forces to the head, various brain injury criteria based on different factors of these kinematics have been developed. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order, 2) the direction and 3) the power of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial

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Concussion and the severity of head impacts in mixed martial arts

Cited by 32 publications

References 52 publications

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

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