Traumatic brain injuries (TBIs) are a major health risk that increases with age. Natural brain aging results in cerebral atrophy and the enlargement of the ventricular regions. The objective of this study is to investigate the effect of cerebral atrophy on brain biomechanics with subject-specific models to determine the risk of traumatic brain injury (TBI). Utilizing subjects from a longitudinal study of aging in healthy volunteers, we created subject-specific brain models of a small cohort with progressive age-related cerebral atrophy. We then simulate concussive loading conditions to study changes in brain deformation, a correlate to risk of TBI. The results display differing trends with increasing ventricle volume, with some subjects exhibiting increases and others showing decreasing strain. Additional subject simulations are needed to clarify these the causes of these trends.
Modeling brain tissue mechanics is important for understanding the pathogenesis of traumatic brain injury, with models often including brain tissue geometry and microstructural features like white matter fiber orientation. Recently, the cerebral vasculature has been included in models, however the effect of cerebral vessels on the mechanical response of the brain is unclear. A dataset of 23 subjects that includes structural MRI, angiography, and mechanical neuroimaging using magnetic resonance elastography (MRE) was collected to determine if there is a dependence of vasculature on in vivo brain mechanical properties. A pipeline was implemented using existing methods for processing anatomical, angiography, and MRE images; all images were co-registered for each subject and transformed to a common space. The regional mean stiffness and damping ratio of the brain, by anatomical segmentation, showed no dependence on vessel density but showed heterogeneity across the brain. A sub-regional analysis after stratifying by MRE stiffness showed a strong positive correlation in the cortical gray matter (R2=0.69) and a strong negative correlation in the deep gray matter (R2=0.76). Other regions showed similar trends with R2 values below 0.54. The opposite trends could be a result of regional microstructure difference, or a dependence on vessel type and size. A similar analysis using the brain damping ratio showed no dependence of vasculature on brain viscous properties. Quantifying the dependence of brain mechanical properties on vasculature will aid in understanding the biomechanics of the brain and inform their use in computational models of brain injury.
The cranial meninges are membranes enveloping the brain. The space between these membranes contains mainly cerebrospinal fluid. It is of interest to study how the volumes of this space change with respect to normal aging. In this work, we propose to combine convolutional neural networks (CNNs) with nested topology-preserving geometric deformable models (NTGDMs) to reconstruct meningeal surfaces from magnetic resonance (MR) images. We first use CNNs to predict implicit representations of these surfaces then refine them with NTGDMs to achieve sub-voxel accuracy while maintaining spherical topology and the correct anatomical ordering. MR contrast harmonization is used to match the contrasts between training and testing images. We applied our algorithm to a subset of healthy subjects from the Baltimore Longitudinal Study of Aging for demonstration purposes and conducted longitudinal statistical analysis of the intracranial volume (ICV) and subarachnoid space (SAS) volume. We found a statistically significant decrease in the ICV and an increase in the SAS volume with respect to normal aging.
Cerebral atrophy is characterized by a shrinking of the brain and consequently an enlargement of fluid-filled spaces within the cranium. It is a hallmark of normal aging and a sequelae following brain injury, and is of relevance in other brain diseases. There has been conflicting evidence of the effect of ventricle enlargement on the biomechanics of the brain during head impact. Computational simulations of brain biomechanics were used to investigate enlargement of the ventricles and subarachnoid space (SAS). These models are summarized as 1) a simplified 2D phantom, 2) an axial 2D brain model, and 3) subject-specific 3D brain model. Our preliminary results with the 2D models show minimal effect of enlarged ventricles on brain deformation, and shows decreasing brain strain with a thicker SAS layer. The 3D models show a general decrease in strain metrics for head motion about all three axes of rotation. Investigating the effect of the size of the fluid-filled spaces within the cranium on brain deformation will aid in the understanding of subject-specific brain injury risk, especially during aging and disease.
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