Mechanical mechanism and indicator of diffuse axonal injury under blast-type acceleration.

Diffuse axonal injury (DAI) caused by acceleration is one of the most prominent forms of blast-induced Traumatic Brain Injury. However, the mechanical mechanism and indicator of axonal deformation-induced injury under blast-type acceleration with high peak and short duration are unclear. This study constructed a multilayer head model that can reflect the response characteristics of translational and rotational acceleration (the peak time of which is within 0.5 ms). Based on von Mises stress, axonal strain and axonal strain rate indicators, the physical process of axonal injury is studied, and the vulnerable area under blast-type acceleration load is given. In the short term (within 1.75 ms), dominated by sagittal rotational acceleration peaks, the constraint of falx and tentorium rapidly imposes the inertial load on the brain tissue, resulting in a high-rate deformation of axons (axonal strain rate of which exceed 100 s-1). For a long term (after 1.75 ms), fixed-point rotation of the brain following the head causes excessive distortion of brain tissue (von Mises stress of which exceeds 15 kPa), resulting in a large axonal stretch strain where the main axonal orientation coincides with the principal strain direction. It is found that the axonal strain rate can better indicate the pathological axonal injury area and coincides with external inertial loading in the risk areas, which suggests that DAI under blast-type acceleration overload is mainly caused by the rapid axonal deformation instead of by the excessive axonal strain. The research in this paper helps understand and diagnose blast-induced DAI.

Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations.

Traumatic brain injury (TBI) is a significant public health burden, and the development of advanced countermeasures to mitigate and prevent these injuries during automotive, sports, and military impact events requires an understanding of the intracranial mechanisms related to TBI. In this study, the efficacy of tissue-level injury metrics for predicting TBI was evaluated using finite element reconstructions from a comprehensive, multi-species TBI database. The database consisted of human volunteer tests, laboratory-reconstructed head impacts from sports, in vivo non-human primate (NHP) tests, and in vivo pig tests. Eight tissue-level metrics related to brain tissue strain, axonal strain, and strain-rate were evaluated using survival analysis for predicting mild and severe TBI risk. The correlation between TBI risk and most of the assessed metrics were statistically significant, but when injury data was analyzed by species, the best metric was often inconclusive and limited by the small datasets. When the human and animal datasets were combined, the injury analysis was able to delineate maximum axonal strain as the best predictor of injury for all species and TBI severities, with maximum principal strain as a suitable alternative metric. The current study is the first to provide evidence to support the assumption that brain strain response between human, pig, and NHP result in similar injury outcomes through a multi-species analysis. This assumption is the biomechanical foundation for translating animal brain injury findings to humans. The findings in the study provide fundamental guidelines for developing injury criteria that would contribute towards the innovation of more effective safety countermeasures.

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain.

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain-skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

In vivo estimates of axonal stretch and 3D brain deformation during mild head impact.

The rapid deformation of brain tissue in response to head impact can lead to traumatic brain injury. In vivo measurements of brain deformation during non-injurious head impacts are necessary to understand the underlying mechanisms of traumatic brain injury and compare to computational models of brain biomechanics. Using tagged magnetic resonance imaging (MRI), we obtained measurements of three-dimensional strain tensors that resulted from a mild head impact after neck rotation or neck extension. Measurements of maximum principal strain (MPS) peaked shortly after impact, with maximal values of 0.019-0.053 that correlated strongly with peak angular velocity. Subject-specific patterns of MPS were spatially heterogeneous and consistent across subjects for the same motion, though regions of high deformation differed between motions. The largest MPS values were seen in the cortical gray matter and cerebral white matter for neck rotation and the brainstem and cerebellum for neck extension. Axonal fiber strain (Ef) was estimated by combining the strain tensor with diffusion tensor imaging data. As with MPS, patterns of Ef varied spatially within subjects, were similar across subjects within each motion, and showed group differences between motions. Values were highest and most strongly correlated with peak angular velocity in the corpus callosum for neck rotation and in the brainstem for neck extension. The different patterns of brain deformation between head motions highlight potential areas of greater risk of injury between motions at higher loading conditions. Additionally, these experimental measurements can be directly compared to predictions of generic or subject-specific computational models of traumatic brain injury.

Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability.

Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle-coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55-70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm3 volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury.

Brain injury tolerance limit based on computation of axonal strain.

Traumatic brain injury (TBI) is the leading cause of death and permanent impairment over the last decades. In both the severe and mild TBIs, diffuse axonal injury (DAI) is the most common pathology and leads to axonal degeneration. Computation of axonal strain by using finite element head model in numerical simulation can enlighten the DAI mechanism and help to establish advanced head injury criteria. The main objective of this study is to develop a brain injury criterion based on computation of axonal strain. To achieve the objective a state-of-the-art finite element head model with enhanced brain and skull material laws, was used for numerical computation of real world head trauma. The implementation of new medical imaging data such as, fractional anisotropy and axonal fiber orientation from Diffusion Tensor Imaging (DTI) of 12 healthy patients into the finite element brain model was performed to improve the brain constitutive material law with more efficient heterogeneous anisotropic visco hyper-elastic material law. The brain behavior has been validated in terms of brain deformation against Hardy et al. (2001), Hardy et al. (2007), and in terms of brain pressure against Nahum et al. (1977) and Trosseille et al. (1992) experiments. Verification of model stability has been conducted as well. Further, 109 well-documented TBI cases were simulated and axonal strain computed to derive brain injury tolerance curve. Based on an in-depth statistical analysis of different intra-cerebral parameters (brain axonal strain rate, axonal strain, first principal strain, Von Mises strain, first principal stress, Von Mises stress, CSDM (0.10), CSDM (0.15) and CSDM (0.25)), it was shown that axonal strain was the most appropriate candidate parameter to predict DAI. The proposed brain injury tolerance limit for a 50% risk of DAI has been established at 14.65% of axonal strain. This study provides a key step for a realistic novel injury metric for DAI.

Head injury assessment of non-lethal projectile impacts: A combined experimental/computational method.

The main objective of this study is to develop a methodology to assess this risk based on experimental tests versus numerical predictive head injury simulations. A total of 16 non-lethal projectiles (NLP) impacts were conducted with rigid force plate at three different ranges of impact velocity (120, 72 and 55m/s) and the force/deformation-time data were used for the validation of finite element (FE) NLP. A good accordance between experimental and simulation data were obtained during validation of FE NLP with high correlation value (>0.98) and peak force discrepancy of less than 3%. A state-of-the art finite element head model with enhanced brain and skull material laws and specific head injury criteria was used for numerical computation of NLP impacts. Frontal and lateral FE NLP impacts to the head model at different velocities were performed under LS-DYNA. It is the very first time that the lethality of NLP is assessed by axonal strain computation to predict diffuse axonal injury (DAI) in NLP impacts to head. In case of temporo-parietal impact the min-max risk of DAI is 0-86%. With a velocity above 99.2m/s there is greater than 50% risk of DAI for temporo-parietal impacts. All the medium- and high-velocity impacts are susceptible to skull fracture, with a percentage risk higher than 90%. This study provides tool for a realistic injury (DAI and skull fracture) assessment during NLP impacts to the human head.

The importance of structural anisotropy in computational models of traumatic brain injury.

Understanding the mechanisms of injury might prove useful in assisting the development of methods for the management and mitigation of traumatic brain injury (TBI). Computational head models can provide valuable insight into the multi-length-scale complexity associated with the primary nature of diffuse axonal injury. It involves understanding how the trauma to the head (at the centimeter length scale) translates to the white-matter tissue (at the millimeter length scale), and even further down to the axonal-length scale, where physical injury to axons (e.g., axon separation) may occur. However, to accurately represent the development of TBI, the biofidelity of these computational models is of utmost importance. There has been a focused effort to improve the biofidelity of computational models by including more sophisticated material definitions and implementing physiologically relevant measures of injury. This paper summarizes recent computational studies that have incorporated structural anisotropy in both the material definition of the white matter and the injury criterion as a means to improve the predictive capabilities of computational models for TBI. We discuss the role of structural anisotropy on both the mechanical response of the brain tissue and on the development of injury. We also outline future directions in the computational modeling of TBI.