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 develop a new brain injury metric, the Brain Angle Metric (BAM), based on the dynamics of a 3 degree-of-freedom lumped parameter brain model. The brain model is built based on the measured natural frequencies of a FE brain model simulated with live human impact data. We show it can be used to rapidly estimate peak brain strains experienced during head rotational accelerations. On our dataset, the simplified model highly correlates with peak principal FE strain (R2=0.80). Further, coronal and axial model displacement correlated with fiber-oriented peak strain in the corpus callosum (R2=0.77). Our proposed injury metric BAM uses the maximum angle predicted by our brain model, and is compared 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 BAM 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 model deviance to those which combined components into a single time trace magnitude. Our brain model can be used in future work both as a computationally efficient alternative to FE models and for classifying injuries over a wide range of loading conditions.
|Original language||English (US)|
|State||Published - Dec 18 2018|
- Brain injury
- Injury criterion
- Injury prediction
ASJC Scopus subject areas