Abstract
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.
Highlights
Computational models incorporating high-fidelity tissue-level anatomy and the mechanical response of various tissues have become a valuable tool for studying the development of traumatic brain injury (TBI)
In an effort to assess the importance of the structural anisotropy in computational models of TBI, we provide a summary of selected studies that have accounted for the structural anisotropy of the white matter in computational head models either through the injury criterion for axonal injury or the material definition of the white matter
Whereas early computational head models of TBI treated the brain as a homogeneous mass and defined injury based on equivalent stress and strain measures, such as the von Mises stress, many recent computational head models of TBI have accounted for the anisotropy of the brain tissue through the use of diffusion tensor imaging
Summary
Computational models incorporating high-fidelity tissue-level anatomy and the mechanical response of various tissues have become a valuable tool for studying the development of traumatic brain injury (TBI). Computational models provide a platform for integrating various mechanical and even biochemical models with the anatomy of the brain Applications of such a computational platform include the ability to predict local deformations and stresses, the likelihood of primary injuries (predominantly with physics-based models), secondary injuries (with inclusion of biochemical models), the likelihood of neurologic outcomes that might occur in diffuse-axonal-injury-related TBI, and the development of kinematic tolerance thresholds for safeguarding from brain injury. The damage might occur predominantly at the cellular level, which is beyond the resolution of many commonly used imaging platforms In such cases, computational models could serve as an invaluable tool for predicting the likelihood of injury. It has been hypothesized that the orientation of the fibers in the white matter play an important role in both the injury development and the mechanical response of the brain tissue. Since the inclusion of structural anisotropy can increase the computational cost and complexity of a model, it is important to assess its impact on the biofidelity and predictive capabilities of the model
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