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A parameterized, or scalable, finite element (FE) model of the human head was developed and validated against the available cadaver experiment data for three impact directions (frontal, occipital and lateral). The brain material properties were modeled using a hyperelastic and viscoelastic constitutive law. The interface between the skull and the brain was modeled in three different ways ranging from purely tied (no-slip) to sliding (free-slip). Two sliding contact definitions were compared with the tied condition. Also, three different stiffness parameters, encompassing the range of published brain tissue properties, were tested. The model using the tied contact definition correlated well with the experimental results for the coup and contrecoup pressures in a frontal impact while the sliding interface models did not. Relative motion between the skull and the brain in lowseverity impacts appears to be relatively insensitive to the contact definitions. It is shown that a range of shear stiffness properties for the brain can be used to model the pressure experiments, while relative motion is a more complex measure that is highly sensitive to the brain tissue properties. Smaller relative motion between the brain and skull results from lateral impact than from a frontal or occipital blow for both the experiments and FE simulations. The material properties of brain tissue are important to the characteristics of relative brain-skull motion. The results suggest that significantly lower values of the shear properties of the human brain than currently used in most three-dimensional (3D) FE models today are needed to predict the localized brain response of an impact to the human head. KEYWORDS-Finite element (FE) analysis; human head; brain displacement; intracranial pressure; brain material properties; brain-skull interface.

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Relative motion between the brain and skull and brain deformation are biomechanics aspects associated with many types of traumatic brain injury (TBI). Thus far, there is only one experimental endeavor reported brain strain under loading conditions commensurate with levels that were capable of producing injury. Most of the existing finite element (FE) head models are validated against brain-skull relative motion and then used for TBI prediction based on strain metrics. However, the suitability of using a model validated against brain-skull relative motion for strain prediction remains to be determined. To partially address the deficiency of experimental brain deformation data, this study revisits the only existing dynamic experimental brain strain data and updates the original calculations, which reflect incremental strain changes. The brain strain is recomputed by imposing the measured motion of neutral density target (NDT) to the NDT triad model. The revised brain strain and the brain-skull relative motion data are then used to test the hypothesis that an FE head model validated against brainskull relative motion does not guarantee its accuracy in terms of brain strain prediction. To this end, responses of brain strain and brain-skull relative motion of a previously developed FE head model (Kleiven, 2007) are compared with available experimental data. CORrelation and Analysis (CORA) and Normalized Integral Square Error (NISE) are employed to evaluate model validation performance for both brain strain and brain-skull relative motion. Correlation analyses (Pearson coefficient) are conducted between average cluster peak strain and average cluster peak brain-skull relative motion, and also between brain strain validation scores and brain-skull relative motion validation scores. The results show no significant correlations, neither between experimentally acquired peaks nor between computationally determined validation scores. These findings indicate that a head model validated against brain-skull relative motion may not be sufficient to assure its strain prediction accuracy. It is suggested that a FE head model with intended use for strain prediction should be validated against the experimental brain deformation data and not just the brain-skull relative motion.