| Literature DB >> 34062102 |
Xianghao Zhan1, Yiheng Li2, Yuzhe Liu1, August G Domel1, Hossein Vahid Alizadeh1, Samuel J Raymond1, Jesse Ruan3, Saeed Barbat3, Stephen Tiernan4, Olivier Gevaert2, Michael M Zeineh5, Gerald A Grant6, David B Camarillo1.
Abstract
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.Entities:
Keywords: brain injury criteria; brain strain; head impact; traumatic brain injury
Mesh:
Year: 2021 PMID: 34062102 PMCID: PMC8169213 DOI: 10.1098/rsif.2021.0260
Source DB: PubMed Journal: J R Soc Interface ISSN: 1742-5662 Impact factor: 4.293