Traumatic brain injury (TBI) is a major problem with an estimated cost of $76 billion per year in the US alone. The Center for Disease Control and Prevention (CDC) documented 2.53 million TBI-related emergency department visits, with approximately 288,000 TBI-related hospitalizations and 56,800 TBI-related deaths in 2014 in the US. The lack of FDA-approved treatment strategies for TBI drives the need for novel therapeutic and preventative measures.
In a quest to reduce TBI-related injuries and deaths, automotive companies have focused their efforts to make safer cars for both occupants and pedestrians. Computational finite element (FE) models have been used to advance research efforts in automotive safety systems engineering in hopes of ameliorating the burden caused by TBI. The current use of FE models in the automotive industry focuses on predicting stresses and strains that occur during the accident itself to predict primary injury. However, contemporary models lack the appropriate mechanical properties required to make accurate predictions of brain tissue deformation after injury and lack the ability to model secondary injuries such as cerebral edema (brain swelling). With cerebral edema being a major cause of death and disability after TBI, and with the pattern and magnitude of cerebral edema being dependent on the initiating strain field in brain tissue during TBI, automotive safety systems could be further improved if 1) FE head models contained more accurate mechanical properties and 2) if FE models could simulate secondary injuries such as cerebral edema. Therefore, the driving purpose of this thesis is two-fold: 1) to determine the mechanical properties of different regions of the brain and 2) to present a new computational methodology that allows for modeling of cerebral edema to better predict patient outcome following TBI.
The use of FE models requires appropriate constitutive formulations and associated parameters to accurately model and predict the initial mechanical response of the brain to injury loading conditions. Since patient outcome is dependent on the resulting strain field within brain tissue post-injury, accurate modeling of brain tissue deformation is important for testing the efficacy of engineered automotive safety systems using FE simulations. To address this need, the first aim of this thesis employed an inverse FE approach to characterize mechanical properties of the human hippocampus (CA1, CA3, dentate gyrus), cortex white matter, and cortex gray matter. Anatomical regions were significantly different in their mechanical properties. Although no sex dependency was observed, there were trends indicating that some male brain regions were generally stiffer than corresponding female regions. In addition, mechanical properties were not dependent on age within the examined age range (4-58 years old). Ultimately, this study provides a structure-specific description of fresh human brain tissue mechanical properties, which will be an important step toward explicitly modeling the heterogeneity of brain tissue deformation during TBI using FE modeling.
Fatal brain injuries may also result from physiological changes in the brain that occur after the primary injury that immediately occurs during head injury. Secondary injuries such as cerebral edema are associated with poor outcome. Despite the severe consequences of cerebral edema, its mechanism is not fully understood. The second aim of this thesis, therefore, was to elucidate the driving mechanism of cerebral edema by demonstrating that cleavage of intracellular fixed-charge density (FCD) reduces brain swelling pressure and to measure the FCD content of rat and pig brain tissue. Thin brain samples were placed into a confined pressure chamber, and FCD content was calculated from measured swelling pressure and the Gibbs-Donnan equation. We observed that cleavage of FCD using enzymes reduced swelling pressure in rat brain tissue samples and determined that pig cortex gray matter contains more FCD than pig cortex white matter. These results demonstrate that cerebral edema may occur in accordance with principles of triphasic swelling biomechanics and demonstrates the plausibility of computationally modeling cerebral edema with triphasic material formulations.
Cerebral edema leads to increased intracranial pressure (ICP) as the brain swells within the fixed volume of the skull, and there is overwhelming evidence of ICP as a powerful predictor of patient outcome following TBI. Current industry standards of patient outcome evaluation use tissue-level metrics solely from primary injury such as maximum principal strain (MPS) or cumulative strain damage measure (CSDM), but these methods can be improved especially in regards to predicting mortality. Therefore, the third aim of this thesis was to develop a new FE head model and computational methodology incorporating triphasic swelling biomechanics to simulate brain swelling following impact to improve patient outcome predictions. Patient outcome was predicted by simulating swelling and calculating the resulting ICP, which is a strong indicator of patient mortality. Calculating ICP in addition to predicting primary injury metrics such as MPS and CSDM may allow automotive safety engineers to make better predictions of patient outcome following TBI so they can develop better safety systems.
Another common indicator of poor outcome following TBI is acute subdural hematoma (ASDH). ASDH is an intracranial bleed that often results from TBI because of stretching and tearing of the bridging veins which causes blood to collect in the innermost layer of the dura. Despite the poor prognosis associated with the presence of ASDH following TBI, the mechanism as to why its presence is associated with a higher likelihood of death remains uncertain. Current state of the art FE head models used in automotive safety engineering efforts do not consider ASDH, which may drastically reduce their effectiveness in predicting patient outcome following TBI. Therefore, the fourth and final aim of this thesis was to incorporate ASDH into our FE head model of swelling and elucidate the underlying secondary brain injury mechanism of ASDH that contributes to increased mortality in hopes of increasing the efficacy of current FE models to predict patient outcome and ultimately design better safety systems. Using our novel FE head model and methodology from aim 3, we showed that the higher likelihood of death associated with the presence of ASDH may be caused by exacerbated ischemic injury which increases ICP, demonstrating that modeling of ASDH is necessary for accurately modeling patient outcome following TBI.
Despite decades of TBI research and FE head model improvements, more work is required to enhance the biofidelity of these models to better predict patient outcome. The work in this thesis is important, as it introduces a new tool that will allow automotive safety engineers to incorporate cerebral edema and ASDH, both of which may drastically influence patient outcome following TBI, into models of head injury to allow for better predictions of patient outcome. It is hoped that the work in this thesis lays the foundation for future work that aids in the design of improved automotive safety systems that will save countless human lives.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/ah78-mh67 |
| Date | January 2023 |
| Creators | Basilio, Andrew Vasco |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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