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 predictability of the BIC on  different types of head impacts has not been evaluated. Physiologically, the brain strain is often considered the key parameter of brain injury. To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at  corpus callosum, and cumulative strain damage (15\%) on  18 BIC  . The results show significant differences in the relationship between BIC and brain strain across datasets, indicating the same BIC value may indicate different brain strain in different head impact subtypes . The accuracy of  regression is generally decreasing if the BIC regression models are fit on a dataset with a different head impact subtype rather than on the dataset with the same subtype . Given this finding, this study raises concerns for applying BIC to predict the brain strain for head impacts different from the head impacts on which the BIC was developed.
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.
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predictability of
accuracy of using
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brain injury risk estimation across
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the
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ability to predict brain strain
risk estimation accuracy
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subtypes
types
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the
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each of
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respectively
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significant differences
a significant difference
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indicate
suggest
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subtypes
types
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brain strain
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head impact subtype
type of head impact
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subtype
type
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predict the brain strain
estimate the brain injury risks