  Traumatic brain injury (TBI) is a complex injury that is hard to predict and
diagnose, with many studies focused on associating head kinematics to brain
injury risk. Recently, there has been a push towards using computationally
expensive finite element (FE) models of the brain to create tissue deformation
metrics of brain injury. Here, we developed a 3 degree-of-freedom
lumped-parameter brain model, built based on the measured natural frequencies
of a FE brain model simulated with live human impact data, to be used to
rapidly estimate peak brain strains experienced during head rotational
accelerations. On our dataset, the simplified model correlates with peak
principal FE strain by an R2 of 0.80. Further, coronal and axial model
displacement correlated with fiber-oriented peak strain in the corpus callosum
with an R2 of 0.77. Using the maximum displacement predicted by our brain
model, we propose an injury criteria and compare it against a number of
existing rotational and translational kinematic injury metrics on a dataset of
head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We
found that our proposed metric performed comparably to peak angular
acceleration, linear acceleration, and angular velocity in classifying injury
and non-injury events. Metrics which separated time traces into their
directional components had improved deviance to those which combined components
into a single time trace magnitude. Our brain model can be used in future work
as a computationally efficient alternative to FE models for classifying
injuries over a wide range of loading conditions.
