Brain multiphysics最新文献

Concussion, in spite of being a mild traumatic brain injury, involves serious long term consequences and can adversely affect the life of an individual, their family and the wider society. Since, diffuse axonal injury (DAI) is known to be one of the most frequent pathological features of traumatic brain injury (TBI), knowledge of the mechanical threshold for concussion in terms of axonal strain can help in developing better brain injury prediction tools in the context of head protection system optimization and the management of sport related concussions. This paper presents development, validation and utilization of an anisotropic viscous hyperelastic finite element rat brain model for investigation of the mechanical threshold for concussion in terms of axonal strain. For the investigation, twenty-six well documented cases of experimental concussion were simulated. A thorough statistical analysis of global kinematic parameters (maximum rotational acceleration and duration) and intra-cerebral parameters (maximum axonal strain, maximum strain energy, maximum von Mises stress, maximum von Mises strain, maximum shear stress, maximum shear strain, maximum principal stress, maximum principal strain, minimum pressure and maximum pressure) revealed that intra-cerebral parameters are better suited for the prediction of concussion than the global kinematic parameters. The estimated tolerance level for a 50% risk of concussion was found to be 8.97% of maximum axonal strain. The results are promising and hence, this study is not only a key step towards better understanding of concussion, but it also contributes towards concussion related investigations.

Statement of Significance

A number of studies have identified axonal strain as one of the key metrics for the prediction of concussion through biomechanical simulations. Where infeasibility of experimentation on in-vivo human brain limits the in-depth investigation, animal models have proved to be efficient. None of the existing finite element rat brain models have taken anisotropy, based on the rat brain DTI, into account, which is rather a crucial aspect for the fidelity. The present study provides a validated anisotropic viscous hyperelastic finite element rat brain model, which was successfully applied for the simulations of experimental concussive loadings on the rat brain and furnished promising results that are in accordance with the literature. As such, it is helpful in developing more accurate brain injury prediction tools in the context of head protection system optimization and for the management of sport related concussions.

Elucidating the mechanisms of mild traumatic brain injuries (mild TBIs), including concussions, is important for developing brain injury criteria and designing head protection devices. Using a finite element (FE) model of the human brain to predict the deformation of the brain parenchyma during a head impact could provide mechanical insights on mild TBIs. However, most conventional brain FE models do not consider how fluid behavior and the perfusion pressure of the cerebrospinal fluid (CSF) will affect brain deformation. This study proposes a novel brain FE model that uses incompressible fluid dynamics (ICFD) to represent the fluid behavior of CSF in the ventricle. In the model with ICFD, the validation accuracy scores on the brain strain during a head impact with a rotational acceleration were significantly higher than those in the model without ICFD. Reconstruction simulations based on two reported mild TBI cases from a rear-end collision and an American football game were conducted using the model with and without ICFD. We found that the maximum principal strain values in the subcortical region and corpus callosum of the model with ICFD were higher and lasted longer than those of the model without ICFD, and this tendency was further enhanced when perfusion pressure was applied. These findings suggested that the fluid behavior and perfusion pressure of the intraventricular CSF could significantly affect the deformation of the brain parenchyma during head impacts. The proposed brain multiphysical FE model could enhance the understanding of mild TBI mechanisms.

Statement of Significance

Mild TBIs or concussions can result from brain deformations caused by the rapid acceleration of the head in the situations such as falls, vehicular accidents, and collisions in sports-related activities. A FE analysis is an effective tool for simulating head impact scenarios associated with mild TBIs and estimating the brain strain. Accurate prediction of mild TBIs requires a brain FE model with high biofidelity. Here, we firstly revealed that the validation accuracy of the model on the brain strain can be improved by considering the fluid behavior of intraventricular CSF. By analyzing existing mild TBI cases using the proposed model, the fluid behavior and perfusion pressure of the CSF were found to significantly affect the brain strain history, resulting in an outcome similar to the clinical symptom. The proposed multiphysical brain model could potentially provide new mechanical insights and further understanding of mild TBIs. Additionally, these findings in this study could be useful in developing brain injury criteria and designing protective equipment.

In cerebrovascular networks, some vertices are more connected to each other than with the rest of the vasculature, defining a community structure. Here, we introduce a class of model networks built by rewiring Random Regular Graphs, which enables reproduction of this community structure and other topological properties of cerebrovascular networks. We use these model networks to study the global flow reduction induced by the removal of a single edge. We analytically show that this global flow reduction can be expressed as a function of the initial flow rate in the removed edge and of a topological quantity, both of which display probability distributions following Cauchy laws, i.e. with large tails. As a result, we show that the distribution of blood flow reductions is strongly influenced by the community structure. In particular, the probability of large flow reductions increases substantially when the community structure is stronger, weakening the network resilience to single capillary occlusions. We discuss the implications of these findings in the context of Alzheimers Disease, in which the importance of vascular mechanisms, including capillary occlusions, is beginning to be uncovered.

Statement of significance

“Occlusions of capillary vessels, the smallest blood vessels in the brain, are involved in major diseases, including Alzheimers Disease and ischemic stroke. To better understand their impact on cerebral blood flow, we theoretically study the vessel network response to a single occlusion. We show that the reduction of blood flow at network scale is a function of the initial blood flow in the occluded vessel and of a topological quantity, both of which have broad distributions, that is, with significant probabilities of extreme values. Using model networks built from Random Regular Graphs, we show that the presence of communities in the network (subparts more connected to each other than with the rest of the vasculature) yield a broader distribution of the topological quantity. This weakens the resilience of brain vessel networks to single capillary occlusions, which may contribute to the pathogenicity of capillary occlusions in the brain”.

We take a data-driven approach to deducing the local volume changes accompanying early development of the fetal human brain. Our approach uses fetal brain atlas MRI data for the geometric changes in representative cases. Using a nonlinear continuum mechanics model of morphoelastic growth, we invert the deformation obtained from MRI registration to arrive at a field for the growth deformation gradient tensor. Our field inversion uses a combination of direct and adjoint methods for computing gradients of the objective function while constraining the optimization by the physics of morphoelastic growth. We thus infer a growth deformation gradient field that obeys the laws of morphoelastic growth. The errors between the MRI data and the forward displacement solution driven by the inverted growth deformation gradient field are found to be smaller than the reference displacement by well over an order of magnitude, and can be driven even lower. The results thus reproduce the three-dimensional growth during the early development of the fetal brain with controllable error. Our findings confirm that early growth is dominated by in plane cortical expansion rather than thickness increase.

Statement of Significance

The points of significance of our work are:

• •

  A data-driven approach to deducing the local volume changes accompanying early development of the fetal human brain from MRI registration.

• •

  The combination of direct and adjoint methods while constraining the optimization by the physics of morphoelastic growth.

• •

  Reproduction of the three-dimensional growth during the early development of the fetal brain with controllable error.

• •

  To our knowledge, the first data-driven confirmation underlying the morphoelastic theory that early growth is dominated by in-plane cortical expansion rather than thickness increase.

• •

  To our knowledge, the first data-driven confirmation underlying the morphoelastic theory that early growth is radially distributed, increasing along the ventricular-cortical direction.

The goal of this study was to measure the mechanical properties of porcine brain tissue and determine if they were dependent on anatomical region or direction. Multistep stress relaxation indentations with a cylindrical probe were performed at 10, 20, and 30% nominal strain on multiple regions in the sagittal, horizontal, and coronal planes. Linear and nonlinear (using the quasilinear theory of viscoelasticity [QLV]) constitutive formulations were applied to extract parameters to capture the mechanical behavior of brain tissue. The linear viscoelastic analytic approach provided the best fit to the experimental data of the models tested. Within each directional plane there were region-dependent differences. The cerebellum was the softest region within each loading direction. Although the majority of the regions were isotropic, the cerebellum white matter and thalamus were anisotropic. The characterization of these mechanical properties can be used to inform finite element models of the pig brain to help predict a more biofidelic response in animal models of traumatic brain injury.

Statement of Significance

Finite element models been developed to predict brain tissue response to traumatic brain injury (TBI) to advance protective and preventative strategies.  In order to improve the accuracy of these computational models, appropriate mechanical experimentation is required to identify brain viscoelasticity, heterogeneity, and anisotropy.  Our custom indentation design allows for high spatial resolution to characterize mechanical properties based on anatomical region and loading direction.  Due to the challenges in procuring human brain tissue, porcine brain models are a suitable substitute to study TBI based on its structural similarities to that of human brains.  This study will further illuminate the complexity of brain tissue mechanics in response to injury loading.

Microvascular blood volume pulsations, combined with CSF circulation result in subtle deformation of the brain during each heartbeat. To visualize and quantify these small deformations, an image processing technique called amplified MRI (aMRI) was recently introduced. aMRI, however, is unable to visualize the 3-directional deformation of the human brain, which is caused by the physiological flow and its biomechanical coupling with the brain. Addressing this issue, we extended 2D aMRI to 3D, which allows visualization of the subtle motion in 3-directions. First, we validated 3D aMRI’s ability to measure out-of-plane motion while simultaneously increasing SNR in digital phantoms mimicking the brain’s deformation. We then applied 2D and 3D aMRI to 3D cine MRI of 6 healthy subjects and found approximately 80% higher temporal SNR in the 3D aMRI outputs with SNR $=26.8\pm 8.3$ compared to the 2D aMRI with SNR $=15.1\pm 2.6$ ($p<0.01$). 3D displacement maps and their dominant modeshapes were extracted, which demonstrated physiologically meaningful patterns of motion in response to heart pulsatility and CSF circulation. We observed the peak superior-inferior displacement near the pons and midbrain. Peak medial-lateral and anterior-posterior displacement were observed close to the $3{}^{rd}$ and lateral ventricles. Interestingly, the modeshapes showed an almost symmetrical expansion of the brain with $33\%\pm 4\%,$ $38\%\pm 4\%,$ and $29\%\pm 7\%$ of the deformation being predominantly towards superior-inferior, anterior-posterior, and medial-lateral, respectively ($p<0.01$). These preliminary results hint at 3D aMRI’s versatility and translatability for providing novel biomechanical imaging markers, which could simplify diagnostics and enable a deeper understanding of the biomechanics of a wide-range of pathophysiological conditions.

Statement of significance

The brain has very soft material properties and is under constant deformation as a result of physiological flow and its biomechanical coupling with the tissue. In this work, a novel image processing algorithm called 3D aMRI is introduced which allows visualization and quantification of this very subtle motion. After validation of the algorithm using digital phantom models, 3D aMRI was applied to in vivo 3D cine MRI data. This allowed measurement of the brain