Traumatic brain injuries (TBI) affect millions of people each year. While research has been dedicated to determining the mechanical properties of the uninjured brain, there has been a lack of investigation on the mechanical properties of the brain after experiencing a primary blast-induced TBI. In this paper, whole porcine brains were exposed to a shock wave to simulate blast-induced TBI. First, ten (10) brains were subjected to unconfined compression experiments immediately following shock wave exposure. In addition, 22 brains exposed to a shock wave were placed in saline solution and refrigerated between 30 minutes and 6.0 hours before undergoing unconfined compression experiments. This study aimed to investigate the effect of a time delay on the viscoelastic properties in the event that an experiment cannot be completed immediately. Samples from both soaked and freshly extracted brains were subjected to compressive rates of 5, 50, and 500 mm/min during the unconfined compression experiments. The fractional Zener (FZ) viscoelastic model was applied to obtain the brain's material properties. The length of time in the solution statistically influenced three of the four FZ coefficients, E 0 (instantaneous elastic response), τ 0 (relaxation time), and α (fractional order). Further, the compressive rate statistically influenced τ 0 and α. AbstractTraumatic brain injuries (TBI) affect millions of people each year. While research has been dedicated to determining the mechanical properties of the uninjured brain, there has been a lack of investigation on the mechanical properties of the brain after experiencing a primary blast-induced TBI. In this paper, whole porcine brains were exposed to a shock wave to simulate blast-induced TBI. First, ten (10) brains were subjected to unconfined compression experiments immediately following shock wave exposure. In addition, 22 brains exposed to a shock wave were placed in saline solution and refrigerated between 30 minutes and 6.0 hours before undergoing unconfined compression experiments. This study aimed to investigate the effect of a time delay on the viscoelastic properties in the event that an experiment cannot be completed immediately. Samples from both soaked and freshly extracted brains were subjected to compressive rates of 5, 50, and 500 mm/min during the unconfined compression experiments. The fractional Zener (FZ) viscoelastic model was applied to obtain the brain's material properties. The length of time in the solution statistically influenced three of the four FZ coefficients, E 0 (instantaneous elastic response), τ 0 (relaxation time), and α (fractional order). Further, the compressive rate statistically influenced τ 0 and α.
Traumatic brain injuries (TBI) affect millions of people each year and can result in long-term difficulties in thinking or focusing. Due to the number of people affected by these injuries, significant research has been dedicated to determining the mechanical properties of the brain using postmortem tissue from animals harvested within 24 hours. The postmortem brain tissue is often stored in a solution until a rheological experiment is ready to begin. However, the effect of storage duration on the mechanical behavior of brain tissue is not understood. In this paper, postmortem porcine brains were placed in normal saline solution (0.9% NaCl) and refrigerated between 30 minutes and 6.5 hours to allow the brain to absorb the solution. Afterwards, samples from both soaked and freshly extracted brains were subjected to unconfined compression tests at compressive rates of 5, 50, and 500 mm/min. The fractional Zener viscoelastic model was applied to obtain the brain's mechanical properties. While the results did not show a significant relationship between absorption and the long-term stiffness (E ∞ ), both the relaxation time (τ 0 ) and fractional order (α) were statistically influenced by both the length of time in the solution and compressive rate. Further, the instantaneous stiffness (E 0 ) was statistically influenced by the length of time in solution, though not the compressive rate.
In the United States, Alzheimer’s disease (AD) affects one in ten people ages 65 and older. In most patients, the first indication of AD is the inability to remember new information, and symptoms grow to include behavior changes and increasing confusion and suspicions surrounding loved ones and daily events. As the disease progresses, the cortex and hippocampus regions of the brain decrease in size, allowing the fluid-filled ventricles within the brain to increase. New and innovative therapies to delay the onset of the disease and progression of the symptoms are being discovered. For example, the antibody solanezumab is undergoing clinical trials to determine its ability to reduce the levels of beta-amyloid in the brain, a known risk factor of AD. Consequently, the ability to identify patients who could benefit from the therapies will be invaluable. The purpose of this study is to determine if the digital volume correlation (DVC) algorithm can detect and track the onset and progression of AD using magnetic resonance imaging (MRI) scans of the head. DVC measures the deformation and strain of the volumetric MRI dataset by tracking the changes in its grey value pattern. A collection of MRI datasets of a patient’s head, which include scans from a baseline visit and visits at 6 months, 12 months, and every 12 months thereafter, is used in our analysis. A strain is applied to each set of MRI scans prior to implementation of the digital volume correlation algorithm. The DVC algorithm is then applied to the dataset and the resulting error between the expected and calculated strain is computed. A decrease in the contrast of the MRI dataset will correlate to additional error by the algorithm. As a result, an increase in the calculated strain error is anticipated to correlate with an increase in the ventricles in the brain, or progression of the disease, over the time period of interest.
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