Multi-Directional Dynamic Model for Traumatic Brain Injury Detection

Abstract

Traumatic brain injury (TBI) is a complex injury that is hard to predict and diagnose, with many studies focused on associating head kinematics to brain injury risk. Recently, there has been a push towards using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we develop a new brain injury metric, the Brain Angle Metric (BAM), based on the dynamics of a 3 degree-of-freedom lumped parameter brain model. The brain model is built based on the measured natural frequencies of a FE brain model simulated with live human impact data. We show it can be used to rapidly estimate peak brain strains experienced during head rotational accelerations. On our dataset, the simplified model highly correlates with peak principal FE strain (R 2 =0.80). Further, coronal and axial model displacement correlated with fiber-oriented peak strain in the corpus callosum (R 2 =0.77). Our proposed injury metric BAM uses the maximum angle predicted by our brain model, and is compared against a number of existing rotational and translational kinematic injury metrics on a dataset of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that BAM performed comparably to peak angular acceleration, linear acceleration, and angular velocity in classifying injury and non-injury events. Metrics which separated time traces into their directional components had improved model deviance to those which combined components into a single time trace magnitude. Our brain model can be used in future work both as a computationally efficient alternative to FE models and for classifying injuries over a wide range of loading conditions. Key words:Brain injury, injury criterion, injury prediction, concussion 159.32, 131.53, and 132.02 respectively. Further, metrics such as HIC and SI which analyze acceleration magnitudes performed with lower sensitivity to those which treated each direction separately. Similarly, the VTCP, which takes into account peak linear and angular acceleration magnitude, had lower model deviance and higher AUCPR and AUCROC than metrics which treated each anatomical direction separately. Peak linear acceleration ( ⃗) had lower deviance, AUCPR, and AUCROC to peak angular kinematics and BAM but still outperformed many other metrics. While many previous studies suggest rotation is a primary cause of brain injury 16,17,56 , the results shown here indicate that linear acceleration still has predictive value in classifying brain injuries. This could be because in our dataset, with the majority of injuries taken from laboratory reconstruction data, the linear and angular acceleration values may be coupled more so than in-vivo data.However, single variate injury criteria, based on linear acceleration, had extremely low sensitivity in our dataset. We see that both HIC15 and SI predict only a single event with >50% risk of injury on our dataset due to a few non-injury events with high HIC15 and SI values. Surprisingly, many existing metrics had hig...

White matter tract-oriented deformation is dependent on real-time axonal fiber orientation

“…62,63 Using these instrumented mouthguards, over 500 head impacts in football have been video confirmed. 64 In the current study, two concussive impacts and ten randomly selected subconcussive impacts 65 were imposed to both the Baseline-model and Truss-embedded-model, respectively, to unravel the deviation of fiber orientation in realistic impacts and its influence on the computation of the tractoriented strain. Of these two concussive impacts, one resulted in the athlete suffering loss of consciousness, while the other was self-reported with post-concussive symptoms.…”

Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator

“…We computed 3DoF relative brain angles using the lumped model proposed in Laksari et al ( 2019 ) to obtain the maximum resultant relative brain angle as a result of each head impact based on the ground truth kinematics and each of the three approximations. In coronal and axial directions, triangular and half-sine approximations gave significantly lower predictions for the brain angle metric, whereas the PCA modes showed no statistically significant difference from the ground truth ( Figure 8 ).…”

“…Given the head kinematics as the base excitation input, these models can estimate the relative motion of brain and skull, particularly the angular motion since that has been seen as the more consequential type of motion (Sullivan et al, 2015 ). Recently, brain angle metric (BAM) was developed based on the characteristics of human brain and skull in finite element simulations, and validated against observed concussive and sub-concussive head impacts (Laksari et al, 2019 ). We compute BAM for each kinematic approximation (PCA, triangle, and half-sine).…”

“…These metrics either use simple discrete mechanical elements in lumped-parameter models, i.e., mass-spring-damper combinations, to give a rigid-body estimate of brain's relative motion with respect to the skull (Kornhauser, 1954 ; Low and Stalnaker, 1987 ; Laksari et al, 2015 ; Gabler et al, 2018b ), or more complex finite element (FE) models with detailed geometry of the brain anatomy, which can simulate the local brain deformation and interaction with the stiff bony or membranous structures (Kleiven, 2013 ; Ji et al, 2014 ; Zhao et al, 2016 ). In the case of lumped models, brain angle metric (BAM), developed based on a data set of concussive and sub-concussive head impacts (Laksari et al, 2019 ), and in the case of FE models, maximum principal strain (MPS) and axonal fiber strain (FS) along the white matter axon fibers have been proposed as effective injury diagnosis metrics (Wu et al, 2019b ).…”

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

“…To quickly estimate the brain injury risk of a head impact, multiple brain injury criteria (BIC) have been developed with reduced-order physical models [6][7][8][9] and statistical model fitting [10,11]. These BIC estimate the risk of brain injury based on the measured kinematics of head movement caused by an impact.…”