Effective Head Impact Kinematics to Preserve Brain Strain

Conventional kinematics-based brain injury metrics often approximate peak maximum principal strain (MPS) of the whole brain but ignore the anatomical location of occurrence. In this study, we develop effective impact kinematics consisting of peak rotational velocity and the associated rotational axis to preserve not only peak MPS but also spatially detailed MPS. A pre-computed brain response atlas (pcBRA) serves as a common reference. A training dataset (N = 3069) is used to develop a convolutional neural network (CNN) to automate impact simplification. When preserving peak MPS alone, the CNN-estimated effective peak rotational velocity achieves a coefficient of determination (R-2) of similar to 0.96 relative to the directly identified counterpart, far outperforming nominal peak velocity from the resultant profiles (R-2 of similar to 0.34). Impacts from a subset of data (N = 1900) are also successfully matched with pcBRA idealized impacts based on elementwise MPS, where their regression slope and Pearson correlation coefficient do not deviate from 1.0 (when identical) by more than 0.1. The CNN-estimated effective peak rotation velocity and rotational axis are sufficiently accurate for similar to 73.5% of the impacts. This is not possible for the nominal peak velocity or any other conventional injury metric. The performance may be further improved by expanding the pcBRA to include deceleration and focusing on region-wise strains. This study establishes a new avenue to reduce an arbitrary head impact into an idealized but actual ``impact mode'' characterized by triplets of basic kinematic variables. They retain specific physical interpretations of head impact and may be an advancement over state-of-the-art kinematics-based scalar metrics for more effective impact comparison in the future.

Automated summary

In this study, effective impact kinematics were developed to preserve detailed brain injury information with the help of a convolutional neural network. The CNN-estimated effective peak rotational velocity outperformed nominal peak velocity in accurately predicting peak MPS. This approach may improve impact comparison and advance kinematics-based injury metrics.