Dynamic characteristics of impact-induced brain strain in the corpus callosum

Abstract

Impact-induced brain strains are spatially rich and intrinsically dynamic. However, the dynamic information of brain strain is not typically used in any injury investigation. Here, we study the dynamic characteristics of maximum and minimum principal strain (maxPS and minPS) of the corpus callosum and highlight the significance of impact simulation time window. Three datasets are used: laboratory reconstructed National Football League (NFL; N=53), measured impacts from Stanford (SF; N=110) and Prevent Biometric (PB; N=314). Impact cases are discarded (by 20.8%, 11.8%, and 66.2%, respectively), when the simulation time window is considered inadequate to capture sufficient strain temporal responses. Fitted Gaussian peaks (with average relative root mean squared error of ∼5% and R2 >0.9) from all datasets have a similar median (15–18 ms) and inter-quantile range (5–9 ms) for the full width at half maximum (FWHM). FWHM significantly and negatively correlates with strain magnitude for NFL and SF, but not for PB. However, ratios between the largest minPS and maxPS magnitudes are similar across datasets (median of 0.5–0.6 with inter-quantile range of 0.2–0.7). Dynamic strain features improve injury prediction. This study motivates further development of advanced deep learning models to instantly estimate the complete details of spatiotemporal history of brain strains, beyond spatially detailed peak strains obtained at maximum values currently available. In addition, this study highlights the time lag between impact kinematics and corpus callosum strain deep in the brain, which has important implications for impact simulation and result interpretation as well as impact sensor designs in the future. Statement of significance•First study to systematically characterize the temporal history of corpus callosum strain in contact sports head impact.•Allows to rapidly launch multiscale modeling of concussion in the corpus callosum without a costly whole brain model simulation.•Motivates further development of advanced deep learning models that will instantly reproduce the complete spatiotemporal details of strain in the entire brain.•Highlights the importance of sufficient impact simulation time window in order to capture the complete strain responses deep in the brain.