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With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (~seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain. [ABSTRACT FROM AUTHOR]

With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (~seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain. [ABSTRACT FROM AUTHOR]

Linking head kinematics to injury risk has been the focus of numerous brain injury criteria. Although many early forms were developed using mechanics principles, recent criteria have been developed using empirical methods based on subsets of head impact data. In this study, a single-degree-of-freedom (sDOF) mechanical analog was developed to parametrically investigate the link between rotational head kinematics and brain deformation. Model efficacy was assessed by comparing the maximum magnitude of displacement to strain-based brain injury predictors from finite element (FE) human head models. A series of idealized rotational pulses covering a broad range of acceleration and velocity magnitudes (0.1-15 krad/s2 and 1-100 rad/s) with durations between 1 and 3000 ms were applied to the mechanical models about each axis of the head. Results show that brain deformation magnitude is governed by three categories of rotational head motion each distinguished by the duration of the pulse relative to the brain's natural period: for short-duration pulses, maximum brain deformation depended primarily on angular velocity magnitude; for long-duration pulses, brain deformation depended primarily on angular acceleration magnitude; and for pulses relatively close to the natural period, brain deformation depended on both velocity and acceleration magnitudes. These results suggest that brain deformation mechanics can be adequately explained by simple mechanical systems, since FE model responses and experimental brain injury tolerances exhibited similar patterns to the sDOF model. Finally, the sDOF model was the best correlate to strain-based responses and highlighted fundamental limitations with existing rotational-based brain injury metrics. [ABSTRACT FROM AUTHOR]