Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain’s (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.
Aging-related periventricular white matter hyperintensities (pvWMHs) are a common observation in medical images of the aging brain. The underlying tissue damage is part of the complex pathophysiology associated with age-related microstructural changes and cognitive decline. PvWMH formation is linked to blood–brain barrier dysfunction from cerebral small vessel disease as well as the accumulation of cerebrospinal fluid in periventricular tissue due to progressive denudation of the ventricular wall. In need of a unifying theory for pvWMH etiology, image-based finite-element modeling is used to demonstrate that ventricular expansion from age-related cerebral atrophy and hemodynamic loading leads to maximum mechanical loading of the ventricular wall in the same locations that show pvWMHs. Ventricular inflation, induced via pressurization of the ventricular wall, creates significant ventricular wall stretch and stress on the ependymal cells lining the wall, that are linked to cerebrospinal fluid leaking from the lateral ventricles into periventricular white matter tissue. Eight anatomically accurate 3D brain models of cognitively healthy subjects with a wide range of ventricular shapes are created. For all models, our simulations show that mechanomarkers of mechanical wall loading are consistently highest in pvWMHs locations (p < 0.05). Maximum principal strain, the ependymal cell thinning ratio, and wall curvature are on average 14%, 8%, and 24% higher in pvWMH regions compared to the remaining ventricular wall, respectively. Computational modeling provides a powerful framework to systematically study pvWMH formation and growth with the goal to develop pharmacological interventions in the future.
The propagation of blast and shock waves in confined environments is a complex phenomenon; yet, being able to derive valid predictions of such phenomena is highly relevant, for example, when it comes to the assessment of protection of personnel in military environments. This study looks at the propagation of blast waves inside a compound survival shelter. Experimental analyses are performed on a small-scale model of the actual configuration of the shelter subjected to the detonation of an explosive charge at different locations close to its entrance. Pressure-time signals are recorded on a number of locations in the model. A numerical model is also developed to complement the experimental program, based on the explicit finite element (FE) code LS-DYNA. The recorded experimental data (e.g., pressure and impulse) are compared with the numerical predictions to validate the FE model. The authors discuss two different modelling approaches (the Lagrangian and the MM-ALE formulations) and analyse the influence of using a different number of ambient layers, the advection method, the time-step size, and level of discretisation. The proposed numerical model predicts and captures the relevant stages of the propagation of the shock wave very well, with error levels on the resulting specific impulse always lower than 19% when compared to the experimental observations. Keywords Blast wave • Shock wave • Small-scale models • Confined explosions • Experimental analysis • Numerical modelling • LS-DYNA
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