@article{ref1,
title="Brain strain: computational model-based metrics for head impact exposure and injury  correlation",
journal="Annals of biomedical engineering",
year="2020",
author="Miller, Logan E. and Urban, Jillian E. and Davenport, Elizabeth M. and Powers, Alexander K. and Whitlow, Christopher T. and Maldjian, Joseph A. and Stitzel, Joel D.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Athletes participating in contact sports are exposed to repetitive subconcussive  head impacts that may have long-term neurological consequences. To better understand  these impacts and their effects, head impacts are often measured during football to  characterize head impact exposure and estimate injury risk. Despite widespread use  of kinematic-based metrics, it remains unclear whether any single metric derived  from head kinematics is well-correlated with measurable changes in the brain. This  shortcoming has motivated the increasing use of finite element (FE)-based metrics,  which quantify local brain deformations. Additionally, quantifying cumulative  exposure is of increased interest to examine the relationship to brain changes over  time. The current study uses the atlas-based brain model (ABM) to predict the strain  response to impacts sustained by 116 youth football athletes and proposes 36 new, or  derivative, cumulative strain-based metrics that quantify the combined burden of  head impacts over the course of a season. The strain-based metrics developed and  evaluated for FE modeling and presented in the current study present potential for  improved analytics over existing kinematically-based and cumulative metrics. Additionally, the findings highlight the importance of accounting for directional  dependence and expand the techniques to explore spatial distribution of the strain  response throughout the brain.<p /> <p>Language: en</p>",
language="en",
issn="0090-6964",
doi="10.1007/s10439-020-02685-9",
url="http://dx.doi.org/10.1007/s10439-020-02685-9"
}