Relationship between brain injury criteria and brain strain across different types of head impacts can be different
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Dec 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 5; 2022: 1; 2023: 1; 2024: 1.
8 papers.
- 2021Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretationcited by 8
- 2021Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematicscited by 3
- 2021Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusioncited by 3
- 2021Data-driven decomposition of brain dynamics with principal component analysis in different types of head impactscited by 2
- 2021Kinematics clustering enables head impact subtyping for better traumatic brain injury predictioncited by 1
- 2022Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural networkcited by 1
- 2023Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptationcited by 0
- 2024Identification of head impact locations, speeds, and force based on head kinematicscited by 0
- 2024Identification of head impact locations, speeds, and force based on head kinematics12 Sep 2024
- 2023Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation8 Jun 2023
- 2022Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network19 Dec 2022
- 2021Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts27 Oct 2021
- 2021Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion31 Aug 2021
- 2021Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction7 Aug 2021
- 2021Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics19 Apr 2021
- 2021Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation9 Feb 2021