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Development of a Metric for Predicting Brain Strain Responses Using Head Kinematics

Overview of attention for article published in Annals of Biomedical Engineering, March 2018
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Title
Development of a Metric for Predicting Brain Strain Responses Using Head Kinematics
Published in
Annals of Biomedical Engineering, March 2018
DOI 10.1007/s10439-018-2015-9
Pubmed ID
Authors

Lee F. Gabler, Jeff R. Crandall, Matthew B. Panzer

Abstract

Diffuse brain injuries are caused by excessive brain deformation generated primarily by rapid rotational head motion. Metrics that describe the severity of brain injury based on head motion often do not represent the governing physics of brain deformation, rendering them ineffective over a broad range of head impact conditions. This study develops a brain injury metric based on the response of a second-order mechanical system, and relates rotational head kinematics to strain-based brain injury metrics: maximum principal strain (MPS) and cumulative strain damage measure (CSDM). This new metric, universal brain injury criterion (UBrIC), is applicable over a broad range of kinematics encountered in automotive crash and sports. Efficacy of UBrIC was demonstrated by comparing it to MPS and CSDM predicted in 1600 head impacts using two different finite element (FE) brain models. Relative to existing metrics, UBrIC had the highest correlation with the FE models, and performed better in most impact conditions. While UBrIC provides a reliable measurement for brain injury assessment in a broad range of head impact conditions, and can inform helmet and countermeasure design, an injury risk function was not incorporated into its current formulation until validated strain-based risk functions can be developed and verified against human injury data.

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Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 24%
Researcher 14 14%
Student > Master 11 11%
Student > Doctoral Student 6 6%
Student > Bachelor 5 5%
Other 7 7%
Unknown 34 34%
Readers by discipline Count As %
Engineering 44 44%
Neuroscience 4 4%
Agricultural and Biological Sciences 2 2%
Medicine and Dentistry 2 2%
Psychology 1 <1%
Other 5 5%
Unknown 43 43%