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

Abstract

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different types of head impacts (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 different types of head impacts 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 each of 18 BIC respectively. The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fit 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.

arXiv comment28 pages, 11 figures

References in corpus (5)

• Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts
• Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion
• Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
• Theoretical and numerical analysis for angular acceleration being determinant of brain strain in mTBI
• Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football

Cited by in corpus (9)

• Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
• Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
• Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
• Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
• Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
• Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network
• Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation
• Identification of head impact locations, speeds, and force based on head kinematics
• A comparison of sports-related head accelerations with and without direct head impacts