A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

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

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

Comparison of dynamic response and maximum principal strain of diagnosed concussion in professional men’s rugby league

Rugby league has been identified as a contact sport with a high incidence of concussion. Research has been conducted to describe incidence and mechanisms of concussion in rugby league, however the risks associated with injury events (shoulder, hip, head to head) are unknown. The purpose of this study was to describe the common injury events leading to concussion in the National Rugby League and compare these events through analysis of dynamic response and brain tissue deformation. Twenty-seven impact videos of concussive injuries were physically reconstructed to obtain linear and rotational accelerations of the head. Dynamic response data were input into the University College Dublin Brain Trauma Model (UCDBTM) to calculate maximum principal strain (MPS). Head-to-head events produced a short duration event with an average peak linear and peak rotational acceleration of 205 g and 15,890 rad/s2, respectively, which were significantly greater than the longer duration hip-to-head (24.7 g and 2650 rad/s2) and shoulder-to-head (24.2 g and 3280 rad/s2) impacts. There were no differences in MPS between events. These results suggest that risk of strain to the brain may be produced by short and long duration acceleration events. Thus, both of these accelerations need to be accounted for in the development of improved head and body protection in rugby. In addition, this data demonstrates that these events cause a risk of concussion requiring efforts to limit or modify how energy is transferred to the head.

Towards a comprehensive delineation of white matter tract-related deformation

Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) has recently been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tracts. Three new tract-related strains were proposed, measuring the normal deformation vertical to the fiber tracts (i.e., tract-vertical strain), and shear deformation along and vertical to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly-proposed strain peaks along with the previously-used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or no-concussion). The results showed that four tract-related strain peaks exhibit superior performance compared to MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.HighlightDeformation of white matte fiber tracts is directly related to brain injury, but only partially analyzed thus far.A theoretical derivation that comprehensively characterizes white matter tract-related deformation is conducted.Analytical formulas of three novel tract-related strains are presented.Tract-related strain peaks are better predictors for concussion than the maximum principal strain.

The Influence of Neck Stiffness on Head Kinematics and Maximum Principal Strain Associated With Youth American Football Collisions

Understanding the relationship between head mass and neck stiffness during direct head impacts is especially concerning in youth sports where athletes have higher proportional head mass to neck strength. This study compared 2 neck stiffness conditions for peak linear and rotational acceleration and brain tissue deformations across 3 impact velocities, 3 impact locations, and 2 striking masses. A pendulum fitted with a nylon cap was used to impact a fifth percentile hybrid III headform equipped with 9 accelerometers and fitted with a youth American football helmet. The 2 neck stiffness conditions consisted of a neckform with and without resistance in 3 planes, representing the upper trapezius, the splenius capitis, and the sternocleidomastoid muscles. Increased neck stiffness resulted in significant changes in head kinematics and maximum principal strain specific to impact velocity, impact location, and striking mass.

Strain Field Evolution Characteristics of Free Surface during Crater Blasting in Sandstone under High Stress

Considering the problems related to hard rock blasting under high in-situ stresses at large depths, we conducted crater blasting tests on sandstone specimens under three static load conditions to investigate the strain field evolution of rock blasting under high stress. The digital image correlation (DIC) technique was used to monitor the evolution of the strain field on the free surface. Thus, the influence of the static stress on the blasting strain field was analyzed, and the formation mechanism of cracks on the free surface was elucidated. The results indicate that a circular tensile strain zone was formed without static loading. The direction of the maximum principal strain was perpendicular to the radius, which lead to the random emergence of multiple radial tensile cracks. Under a uniaxial static loading, an elliptical tensile strain zone was formed. The direction of the maximum principal strain was perpendicular to the static loading direction. This facilitated the initiation and propagation of tensile cracks preferentially in the direction parallel to the static loading. Under an equal biaxial static loading, the initial compressive strain in the specimen reduced the increment rate of the blasting strain, and restrained the formation of surface cracks. Besides, a determination method for dynamic tensile fracture strain of rock was proposed.

The relationship between directional components of dynamic response and maximum principal strain for impacts to an American football helmet

Impact parameters used to design the American football helmet and the parameters associated with mechanisms of concussive injury are not consistent. Head impacts resulting in concussive injury in football are characterized as events creating rotational motion of the head that generate brain tissue strain. The extent of tissue strain influences the resulting severity of injury. Helmet technology aimed to decrease brain tissue strain by reducing the extent of brain motion could help reduce injury risk. Current helmet performance and evaluation measures, such as peak resultant of linear and rotational acceleration, do not fully define directional brain motion and therefore cannot provide sufficient information for this type of improvement. This study was conducted to determine whether coordinate components (X, Y, and Z) of linear and rotational acceleration would correlate with maximum principal strain, a common measure of brain injury risk. Coordinate components define directional motion of the head and offer a specific design parameter more easily reduced using engineered structures than peak resultant acceleration. In addition to coordinate components, this study introduces the dominant component, defined as the coordinate component with the highest contribution to the resultant acceleration, for additional evaluation. The results show that the relationship between the X, Y, and Z coordinate components of acceleration and maximum principal strain is location- and direction-dependent. The study indicates a strong relationship between the peak resultant and dominant components of acceleration to maximum principal strain. Because the dominant component of acceleration accounts for direction and location, identifying the relationship between dominant acceleration and maximum principal strain demonstrates the potential use of this metric to improve future helmet innovation aimed at reducing tissue strain.