Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation

Abstract

Both linear $(\mathbf{a}_{\mathrm{lin}})$ and rotational $(\mathbf{a}_{\mathrm{rot}} )$ accelerations contribute to head impacts on the field in contact sports; however, they are often isolated in injury studies. It is critical to evaluate the feasibility of estimating brain responses using isolated instead of full degrees-of-freedom (DOFs) accelerations. In this study, we investigated the sensitivities of regional brain strain-related responses to resultant $\mathbf{a}_{\mathrm{lin}}$ and $\mathbf{a}_{\mathrm{rot}}$ as well as the relative contributions of these acceleration components to the responses via random sampling and linear regression using parameterized, triangulated head impacts with kinematic variable values based on on-field measurements. Two independently established and validated finite element models of the human head were employed to evaluate model-consistency and dependency in results: the Dartmouth Head Injury Model and Simulated Injury Monitor. For the majority of the brain, volume-weighted regional peak strain, strain rate, and von Mises stress accumulated from the simulation significantly correlated with the product of the magnitude and duration of $\mathbf{a}_{\mathrm{rot}}$, or effectively, the rotational velocity, but not to $\mathbf{a}_{\mathrm{lin}}$. Responses from $\mathbf{a}_{\mathrm{rot}}$-only were comparable to the full-DOF counterparts especially when normalized by injury-causing thresholds (e.g., volume fractions of large differences virtually diminished (i.e., $<$1 %) at typical difference percentage levels of 1–4 % on average). These model-consistent results support the inclusion of both rotational acceleration magnitude and duration into kinematics-based injury metrics and demonstrate the feasibility of estimating strain-related responses from isolated $\mathbf{a}_{\mathrm{rot}}$ for analyses of strain-induced injury relevant to contact sports without significant loss of accuracy, especially for the cerebrum.

Appendix: Description of the Dartmouth Head Injury Model (DHIM) and its validation

Briefly, the DHIM was created based on a template high-resolution T1-weighted MRI of an athlete clinically diagnosed with concussion whose head was positioned neutrally without tilting in MRI. The methodology of model creation was described in detail in (Zhao et al. 2012). All solid (surface) parts were represented by hexahedral (quadrilateral) elements. A reduced integration with hourglass control was used (hourglass energy typically 5–8 % of internal energy), and all anatomical interfaces were modeled as sharing nodes (Takhounts et al. 2008). Because of the soft CSF layer between the brain surface and all of its surrounding structures (falx, tentorium, dura), interfacial sliding of the brain was possible. The material properties of the brain were identical to the “average model” in Kleiven (2007) and were reported in (Ji et al. 2014b) along with the material properties for other components. In total, the model contains 101,420 nodes and 115,228 elements with a combined mass of 4.562 kg for the head, and 56,632 nodes and 55,062 elements with a combined mass of 1.436 kg for the brain.

The DHIM was validated against brain–skull relative displacements measured in three representative cadaveric head impacts (frontal (C383-T1), occipital (C755-T2), and parietal (C394-T4); Hardy et al. (2001, 2007); model was scaled to match the reported cadaveric head dimension when possible). The performance was quantified in terms of correlation score (Kimpara et al. 2006) and correlation coefficient (Kleiven 2006). The average correlation score for DHIM was 83.37 (see Table 3), which was comparable to that of Total HUman Model for Safety (THUMS; average score of 85.52; Kimpara et al. 2006). Both models were categorized as “good” or nearly “excellent” according to a fidelity rating (Lange et al. 2005). In addition, the validation performance of DHIM was also comparable to that in Kleiven 2006 in terms of average correlation coefficient (0.84 and 0.73 vs. 0.63 and 0.78 for the frontal and occipital impacts, respectively). Comparison between model-estimated brain responses and the measurements is given in Figs. 9, 10 and 11 (results for the parietal impact only available for DHIM).

Fig. 9

Comparison of model-estimated relative brain–skull displacements with those measured for selected neutral density target (NDT) locations (a1, a6, p1, and p6) in a frontal impact (C383-T1)

Fig. 10

Comparison of model-estimated relative brain–skull displacements with those measured for selected NDT locations (a1, a5, p1, and p5) in an occipital impact (C755-T2)

Fig. 11

Comparison of the DHIM-estimated relative brain–skull displacements with those measured for selected NDT locations (4 and 11) in a parietal impact (C394-T4)

Table 3 Summary of model validation performances of the DHIM and THUMS against brain–skull relative displacements measured from representative cadaveric head impacts in terms of correlation score in phase, amplitude, and shape (Kimpara et al. 2006)