Brain multiphysics最新文献

Sports-related mild traumatic brain injury (mTBI) is a growing public health concern, affecting millions in the U.S., annually. Current helmets are primarily designed to mitigate head kinematics, despite the importance of the brain substructures mechanics in mTBI mechanism. Therefore, it is crucial to consider the dynamical behavior of brain substructures, which has been shown in prior studies to be associated with strain concentration. Here, we studied the modal behavior and strain patterns of the substructures of the brain finite element (FE) model through Dynamic Mode Decomposition. We conducted side and front impact pendulum tests on a dummy headform equipped with hockey, football, ski, and bicycle helmets. After simulating the impact tests using a brain FE model, we calculated the dynamic modes of this computational model for the whole brain, corpus callosum, brainstem, and cerebellum. The main mode of oscillation in all regions for all helmet types occurred around the frequency regime of 7–15 Hz. Also, in cerebellum, a second harmonic was observed at 40–50 Hz in front impact, and 38 and 62 Hz in side impact in bicycle and ski helmets, respectively. Furthermore, we analyzed the correlation between the modal response and peak maximum principal strain (MPS). These analyses mostly showed a direct association between the computational modal behavior and MPS, where helmet tests with closely spaced modes and high-frequency modal amplitudes led to higher MPS values. This association between the computational modal behavior and strain patterns demonstrated a potential for improving helmet designs through a novel design objective.

Background

The pathophysiology of Idiopathic Intracranial Hypertension (IIH) is poorly understood, making the disease difficult to properly diagnose and treat. Endovascular venous stenting has emerged as an effective non-invasive treatment option for a select cohort of IIH patients with venous sinus stenosis and elevated venous sinus pressure gradient. Unfortunately, current methods of determining patient eligibility for stenting treatment depend on highly invasive and insufficient measurement methods such as venous manometry, which can only measure pressure gradients and not other components of the complex 3D hemodynamic environment. Thus, there is a need for a non-invasive methodology for determining the 3D flow environment of the dural venous sinuses.

Objective

To develop a novel method of non-invasive, patient-specific computational fluid dynamic (CFD) simulation of venous sinus hemodynamics for evaluating stenting eligibility.

Method

A patient with IIH and elevated sinus pressure gradient underwent MR venography, phase-contrast MR venography, and venous manometry. Patient-specific dural venous anatomy was segmented from the MR venography to construct 3D models of the venous sinuses. 3D transient patient-specific computational fluid dynamic simulations were conducted using flow velocities measured with phase-contrast MR venography as boundary conditions.

Results

Successful computational simulations were completed, allowing for the calculation of the spatio-temporal evolution of blood flow through the dural venous sinuses, and the quantitative examination of pressure gradients. Calculated pressure gradients from CFD were validated against venous manometry with an error of only ∼5%.

Conclusions

We have successfully developed time-resolved, patient-specific 3D computational simulations of the dural venous sinuses without assumptions at the boundary conditions for the first time. The methodology can accurately and non-invasively measure venous pressure gradients. This preliminary study serves as a proof of concept for our method to be used as a diagnostic tool for determining venous stenting eligibility, as well as a tool for advancing the general understanding of IIH pathophysiology.

Cortical folds, known as gyri and sulci, are prominent features of the human brain that play a crucial role in its function. These folds exhibit both consistency and variation within and across individuals and species, presenting a scientific challenge to our understanding of the underlying mechanisms. In this perspective paper, we summarize current knowledge about fold development and placement. We discuss the temporal and anatomical differences between primary, secondary, and tertiary folds, highlighting the consistency of primary folds and the increasing variation in later-developing folds. We explore the biological and mechanical factors that influence fold placement, including gene expression, tissue growth, axonal tension, curvature, thickness, and stiffness, which likely work together in a complex, coupled manner. We also highlight the need for advanced computational modeling approaches to unravel the mechanisms of precise placement of primary folds and further our understanding of brain complexity.

Statement of significance: Understanding the factors driving both the consistency and variation in fold patterns is essential for unraveling the functional implications and potential links to neurological and psychiatric disorders. Ultimately, gaining deeper insights into fold development and placement could have significant implications for our fundamental understanding of the brain, as well as mental health research and clinical applications.

Endovascular techniques, such as endoluminal or endosaccular reconstruction, have emerged as the preferred method for treating both ruptured and unruptured intracranial aneurysms, replacing open surgery in most cases. The minimally invasive approach has been shown to result in better surgical outcomes and lower mortality rates. Before the procedure, neuroradiologists rely only on their experience and visual aids from medical imaging techniques to select the appropriate endovascular option, device model and size for each patient. Despite the benefits of endovascular techniques, significant complications can arise during and after the procedures, including intraprocedural aneurysm perforation, delayed rupture, aneurysm regrowth, in-stent restenosis and thromboembolic events. Therefore, predictive virtual replicas of these interventions can serve as a valuable tool to assist neuroradiologists in the decision-making process and optimise treatment success, especially in cases involving complex geometries. Computational modelling can enable the simulation of different treatment strategies considering the most clinically relevant short- and long-term outcomes of the deployment and the postoperative complications that may arise over time.

Statement of significance: This review explores the state of the art in modelling the mechanics of the main neurovascular devices, their deployment within patient-specific geometries, their interaction with the vessel wall and their influence on the local hemodynamics. As it strongly affects their applicability in clinical practice, particular attention is paid to the computational accuracy and efficiency of the different modelling strategies. The aim is to evaluate how these scientific tools and discoveries can support practitioners in making informed decisions and highlight the challenges that require further study.

Periventricular white matter hyperintensities (WMH) are a common finding in medical images of the aging brain and are associated with white matter damage resulting from cerebral small vessel disease, white matter inflammation, and a degeneration of the lateral ventricular wall. Despite extensive work, the etiology of periventricular WMHs remains unclear. We pose that there is a strong coupling between age-related ventricular expansion and the degeneration of the ventricular wall which leads to a dysregulated fluid exchange across this brain–fluid barrier. Here, we present a multiphysics model that couples cerebral atrophy-driven ventricular wall loading with periventricular WMH formation and progression. We use patient data to create eight 2D finite element models and demonstrate the predictive capabilities of our damage model. Our simulations show that we accurately capture the spatiotemporal features of periventricular WMH growth. For one, we observe that damage appears first in both the anterior and posterior horns and then spreads into deeper white matter tissue. For the other, we note that it takes up to 12 years before periventricular WMHs first appear and derive an average annualized periventricular WMH damage growth rate of 15.2 ± 12.7 mm${}^2$/year across our models. A sensitivity analysis demonstrated that our model parameters provide sufficient sensitivity to rationalize subject-specific differences with respect to onset time and damage growth. Moreover, we show that the septum pellucidum, a membrane that separates the left and right lateral ventricles, delays the onset of periventricular WMHs at first, but leads to a higher WMH load in the long-term.

Statement of Significance: Brain aging is accompanied by many structural and functional changes. In nearly all aged brains, white matter lesions appear in periventricular and diffuse subcortical regions which are associated with progressive functional decline. In our work, we present a multiphysics model that not only predicts the onset location of periventricular white matter lesions but also their subsequent growth as a result of age-related cerebral atrophy and ventricular enlargement. Our model provides a mechanics-based rationale for their characteristic spatiotemporal progression patterns and will allow to identify at-risk subjects for early lesion formation.

The majority of human brain folding occurs during the third trimester of gestation. Although many studies have investigated the physical mechanisms of brain folding, a comprehensive understanding of this complex process has not yet been achieved. In mechanical terms, the “differential growth hypothesis” suggests that the formation of folds results from a difference in expansion rates between cortical and subcortical layers, which eventually leads to mechanical instability akin to buckling. It has also been observed that axons, a substantial component of subcortical tissue, can elongate or shrink under tensile or compressive stress, respectively. Previous work has proposed that this cell-scale behavior in aggregate can produce stress-dependent growth in the subcortical layers. The current study investigates the potential role of stress-dependent growth on cortical surface morphology, in particular the variations in folding direction and curvature over the course of development. Evolution of sulcal direction and mid-cortical surface curvature were calculated from finite element simulations of three-dimensional folding in four different initial geometries: (i) sphere; (ii) axisymmetric oblate spheroid; (iii) axisymmetric prolate spheroid; and (iv) triaxial spheroid. The results were compared to mid-cortical surface reconstructions from four preterm human infants, imaged and analyzed at four time points during the period of brain folding. Results indicate that models incorporating subcortical stress-dependent growth predict folding patterns that more closely resemble those in the developing human brain.

Statement of Significance

Cortical folding is a critical process in human brain development. Aberrant folding is associated with disorders such as autism and schizophrenia, yet our understanding of the physical mechanism of folding remains limited. Ultimately mechanical forces must shape the brain. An important question is whether mechanical forces simply deform tissue elastically, or whether stresses in the tissue modulate growth. Evidence from this paper, consisting of quantitative comparisons between patterns of folding in the developing human brain and corresponding patterns in simulations, supports a key role for stress-dependent growth in cortical folding.

The human brain remains an endless source of wonder and represents an intruiging scientific frontier. Multiphysics approaches naturally lend themselves to combine our extensive knowledge about the neurobiology of aging and diseases with mechanics to better capture the multiscale behavior of the brain. Our group uses experimental methods, medical image analysis, and constitutive modeling to develop better disease models with the long-term goal to improve diagnosis, treatment, and ultimately enable prevention of many prevalent age- and disease-related brain changes. In the present perspective, we outline on-going work related to neurodevelopment, aging, and neurodegenerative disease.