Abstract Traumatic brain injury (TBI), particularly from explosive blasts, is a major cause of casualties in modern military conflicts. Computational models are an important tool in understanding the underlying biomechanics of TBI but are highly dependent on the mechanical properties of soft tissue to produce accurate results. Reported material properties of brain tissue can vary by several orders of magnitude between studies, and no published set of material parameters exists for porcine brain tissue at strain rates relevant to blast. In this work, brain tissue from the brainstem, cerebellum, and cerebrum of freshly euthanized adolescent male Göttingen minipigs was tested in simple shear and unconfined compression at strain rates ranging from quasi-static (QS) to 300 s−1. Brain tissue showed significant strain rate stiffening in both shear and compression. Minimal differences were seen between different regions of the brain. Both hyperelastic and hyper-viscoelastic constitutive models were fit to experimental stress, considering data from either a single loading mode (unidirectional) or two loading modes together (bidirectional). The unidirectional hyper-viscoelastic models with an Ogden hyperelastic representation and a one-term Prony series best captured the response of brain tissue in all regions and rates. The bidirectional models were generally able to capture the response of the tissue in high-rate shear and all compression modes, but not the QS shear. Our constitutive models describe the first set of material parameters for porcine brain tissue relevant to loading modes and rates seen in blast injury.
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Hyperelastic material properties of axonal fibers in brain white matter
Accurate characterization of the mechanical properties of the human brain at both microscopic and macroscopic length scales is a critical requirement for modeling of traumatic brain injury and brain folding. To date, most experimental studies that employ classical tension/compression/shear tests report the mechanical properties of the brain averaged over both the gray and white matter within the macroscopic regions of interest. As a result, there is a missing correlation between the independent mechanical properties of the microscopic constituent elements and the composite bulk macroscopic mechanical properties of the tissue. This microstructural computational study aims to inversely predict the hyperelastic mechanical properties of the axonal fibers and their surrounding extracellular matrix (ECM) from the bulk tissue's mechanical properties. We develop a representative volume element (RVE) model of the bulk tissue consisting of axonal fibers and ECM with the embedded element technique. A multiobjective optimization technique is implemented to calibrate the model and establish the independent mechanical properties of axonal fibers and ECM based on seven previously reported experimental mechanical tests for bulk white matter tissue from the corpus callosum. The result of the study shows that the discrepancy between the reported values for the elastic behavior of white matter in literature stems from the anisotropy of the tissue at the microscale. The shear modulus of the axonal fiber is seven times larger than the ECM, with axonal fibers that also show greater nonlinearity, contrary to the common assumption that both components exhibit identical nonlinear characteristics.
Statement of significance
The reported mechanical properties of white matter microstructure used in traumatic brain injury or brain mechanics studies vary widely, in some cases by up to two orders of magnitude. Currently, the material parameters of the white matter microstructure are identified by a single loading mode or ultimately two modes of the bulk tissue. The presented material models only define the response of the bulk and homogenized white matter at a macroscopic scale and cannot explicitly capture the connection between the material properties of microstructure and bulk structure. To fill this knowledge gap, our study characterizes the hyperelastic material properties of axonal fibers and ECM using microscale computational modeling and multiobjective optimization. The hyperelastic material properties for axonal fibers and ECM presented in this study are more accurate than previously proposed because they have been optimized using seven or six loading modes of the bulk tissue, which were previously limited to only two of the seven possible loading modes. As such, the predicted values with high accuracy could be used in various computational modeling studies. The systematic characterization of the material properties of the human brain tissue at both macro- and microscales will lead to more accurate computational predictions, which will enable a better understanding of injury criteria, and has a positive impact on the improved development of smart protection systems, and more accurate prediction of brain development and disease progression.
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- Award ID(s):
- 2123061
- PAR ID:
- 10324993
- Date Published:
- Journal Name:
- Brain multiphysics
- Volume:
- 2
- ISSN:
- 2666-5220
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Finite element analysis is used to study brain axonal injury and develop Brain White Matter (BWM) models while accounting for both the strain magnitude and the strain rate. These models are becoming more sophisticated and complicated due to the complex nature of the BMW composite structure with different material properties for each constituent phase. State-of-the-art studies focus on employing techniques that combine information about the local axonal directionality in different areas of the brain with diagnostic tools such as Diffusion-Weighted Magnetic Resonance Imaging (Diffusion-MRI). The diffusion-MRI data offers localization and orientation information of axonal tracks which are analyzed in finite element models to simulate virtual loading scenarios. Here, a BMW biphasic material model comprised of axons and neuroglia is considered. The model’s architectural anisotropy represented by a multitude of axonal orientations, that depend on specific brain regions, adds to its complexity. During this effort, we develop a finite element method to merge micro-scale Representative Volume Elements (RVEs) with orthotropic frequency domain viscoelasticity to an integrated macro-scale BWM finite element model, which incorporates local axonal orientation. Previous studies of this group focused on building RVEs that combined different volume fractions of axons and neuroglia and simulating their anisotropic viscoelastic properties. Via the proposed model, we can assign material properties and local architecture on each element based on the information from the orientation of the axonal traces. Consecutively, a BWM finite element model is derived with fully defined both material properties and material orientation. The frequency-domain dynamic response of the BMW model is analyzed to simulate larger scale diagnostic modalities such as MRI and MRE.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1016/j.brain.2021.100035
