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Traumatic brain injury (TBI) is a complex injury that is hard to predict anddiagnose, with many studies focused on associating head kinematics to braininjury risk. Recently, there has been a push towards using computationallyexpensive finite element (FE) models of the brain to create tissue deformationmetrics of brain injury. Here, we developed a 3 degree-of-freedomlumped-parameter brain model, built based on the measured natural frequenciesof a FE brain model simulated with live human impact data, to be used torapidly estimate peak brain strains experienced during head rotationalaccelerations. On our dataset, the simplified model correlates with peakprincipal FE strain by an R2 of 0.80. Further, coronal and axial modeldisplacement correlated with fiber-oriented peak strain in the corpus callosumwith an R2 of 0.77. Using the maximum displacement predicted by our brainmodel, we propose an injury criteria and compare it against a number ofexisting rotational and translational kinematic injury metrics on a dataset ofhead kinematics from 27 clinically diagnosed injuries and 887 non-injuries. Wefound that our proposed metric performed comparably to peak angularacceleration, linear acceleration, and angular velocity in classifying injuryand non-injury events. Metrics which separated time traces into theirdirectional components had improved deviance to those which combined componentsinto a single time trace magnitude. Our brain model can be used in future workas a computationally efficient alternative to FE models for classifyinginjuries over a wide range of loading conditions.

Multi-directional dynamic model for traumatic brain injury detection