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BackgroundThe prediction of rupture in intracranial aneurysms is challenging. Aneurysm growth has been identified as a strong risk factor for rupture and aneurysm wall motion is a potential biomarker for growth, but visualizing aneurysm wall motion using conventional imaging techniques is difficult. Computational fluid dynamic simulations have been used to identify hemodynamic risk factors of intracranial aneurysm instability, but often lack observable and quantifiable biomechanical correlates that can be directly measured in vivo. MethodsIn this retrospective case–control study of matched patients, cohorts with growing (n=6) and stable (n=6) unruptured intracranial aneurysms were selected from our institutional database of 4D Flow MRI scans. The amplified Flow algorithm was used to extract maps of wall motion for each aneurysm. Hemodynamics within the aneurysm dome were calculated using established computational fluid dynamic methods, and hemodynamic variables were evaluated against wall motion for stable and growing aneurysms. ResultsSeveral hemodynamic variables were found to be both significant predictors of aneurysm growth and highly correlated with aneurysm wall motion. The hemodynamic variable most correlated with both the maximum value of aneurysm wall motion and spatial variance of aneurysm wall motion, the time coefficient of variance of the directional wall shear stress gradient (representing changing directions of wall shear stress), was also the best hemodynamic predictor of aneurysm growth. ConclusionsSpatial variance of wall motion and hemodynamic variables are increased in growing aneurysms, and the fluctuations in the directional wall shear stress correlate directly with wall motion, indicating that heterogeneous wall motion and hemodynamics are interrelated and play a critical role in aneurysm instability. 

Abstract Traumatic brain injury (TBI) is often associated with microstructural tissue damage in the brain, which results from its complex biomechanical behavior. Recent studies have shown that the deep white matter (WM) region of the human brain is susceptible to being damaged due to strain localization in that region. Motivated by these studies, in this paper, we propose a geometrically nonlinear dynamical reduced order model (ROM) to model and study the dynamics of the deep WM region of the human brain under coronal excitation. In this model, the brain hemispheres were modeled as lumped masses connected via viscoelastic links, resembling the geometry of the corpus callosum (CC). Employing system identification techniques, we determined the unknown parameters of the ROM, and ensured the accuracy of the ROM by comparing its response against the response of an advanced finite element (FE) model. Next, utilizing modal analysis techniques, we determined the energy distribution among the governing modes of vibration of the ROM and concluded that the demonstrated nonlinear behavior of the FE model might be predominantly due to the special geometry of the brain deep WM region. Furthermore, we observed that, for sufficiently high input energies, high frequency harmonics at approximately 45 Hz, were generated in the response of the CC, which, in turn, are associated with high-frequency oscillations of the CC. Such harmonics might potentially lead to strain localization in the CC. This work is a step toward understanding the brain dynamics during traumatic injury.