Brain multiphysics最新文献

The axonal cortex is composed of a regular structure of F-actin and spectrin able to contract thanks to myosin II motors. Such an active tension is of fundamental importance in controlling the physiological shape of axons. Recent experiments show that axons modulate the contraction of the cortex when subject to mechanical deformations, exhibiting a non-trivial coupling between the hoop and the axial active tension. However, the underlying mechanisms are still poorly understood. In this paper, we propose a continuum model of the axon based on the active strain theory. By using the Coleman–Noll procedure, we shed light on the coupling between the hoop and the axial active strain through the Mandel stress tensor. We propose a qualitative analysis of the system under the simplifying assumption of incompressibility, showing the existence of a stable equilibrium solution. In particular, our results show that the axon regulates the active contraction to maintain a homeostatic stress state. Finally, we propose numerical simulations of the model, using a more suitable compressible constitutive law. The results are compared with experimental data, showing an excellent quantitative agreement.

Statement of Significance The mechanics of cortical contractility in axons is still poorly understood. Unravelling the mechanisms underlying axial and hoop stress generation in the cortex will give insight on the active regulation of axon diameter. The understanding of this phenomenon may shed new light on the physical causes of axonal morphological degeneration as a consequence of neurodegenerative diseases, viral infections, and traumatic brain injuries.

Magnetic resonance elastography (MRE) is an MRI technique for imaging the mechanical properties of brain in vivo, and has shown differences in properties between neuroanatomical regions and sensitivity to aging, neurological disorders, and normal brain function. Past MRE studies investigating these properties have typically assumed the brain is mechanically isotropic, though the aligned fibers of white matter suggest an anisotropic material model should be considered for more accurate parameter estimation. Here we used a transversely isotropic, nonlinear inversion algorithm (TI-NLI) and multi-excitation MRE to estimate the anisotropic material parameters of individual white matter tracts in healthy young adults. We found significant differences between individual tracts for three recovered anisotropic parameters: substrate shear stiffness, $\mu$ (range: 2.57 – 3.02 kPa), shear anisotropy, $\phi$ (range: -0.026 – 0.164), and tensile anisotropy, $\zeta$ (range: 0.559 – 1.049). Additionally, we demonstrated the repeatability of these parameter estimates in terms of lower variability of repeated measures in a single subject relative to variability in our sample population. Further, we observed significant differences in anisotropic mechanical properties between segments of the corpus callosum (genu, body, and splenium), which is expected based on differences in axonal microstructure. This study shows the ability of MRE with TI-NLI to estimate anisotropic mechanical properties of white matter and presents reference properties for tracts throughout the healthy brain.

Statement of significance

In this study we use magnetic resonance elastography to determine the mechanical properties of white matter, which can be useful in characterizing neurological conditions such as multiple sclerosis and traumatic brain injury. However, due to its fibrous nature, accurate estimation of mechanical properties of white matter requires an anisotropic material model. In this work, we use a transversely isotropic inversion algorithm with data from multi-excitation MRE to determine the anisotropic mechanical properties of white matter in a healthy young population based upon an anisotropic material model. We display the ability of MRE to capture structural differences between different white matter tracts and sub-regions of these tracts, which are expected to reflect differences such as average axon thickness and myelin density. This robust estimation of white matter anisotropic properties in a young, healthy population provides an avenue for future studies to implement these methods to examine brain development, aging, and pathology.

This study presents an in silico finite element (FE) model-based biomechanical analysis of brain injury metrics and associated risks of a soccer ball impact to the head for aware and unaware athletes, considering ball impact velocity and direction. The analysis presented herein implements a validated soccer ball and 50^th percentile human head computational FE model for quantifying traumatic brain injury (TBI) metrics. The brain's mechanical properties are designated using a viscoelastic-viscoplastic constitutive material model for the white and gray matter within the human head FE model. FE results show a dynamic human head-soccer ball peak contact area of approximately seven times greater than those documented for helmet-to-helmet hits in American Football. Due to the deformable nature of the soccer ball, the impact dynamics are unique depending on the location and velocity of impact. TBI injury risks also depend on the location of impact and the impact velocity. Impacts to the rear (BrIC:0.48, HIC₁₅:180.7), side (BrIC:0.52, HIC₁₅:176.5), and front (BrIC:0.37, HIC₁₅:129.0) are associated with the highest injury risks. Furthermore, the FE results indicate when an athlete is aware of an incoming ball, HIC₁₅-based Abbreviated Injury Scale 1 (AIS 1) injury risks for the front, side, and rear impacts decrease from 10.5%, 18.5%, and 19.3%, respectively, to approximately 1% in front and side impacts and under 6% in a rear impact. Lastly, the unique contact area between the head and soccer ball produces pressure gradients in the ball that translate into distinguishable stress waves in the skull and the cerebral cortex.

Statement of significance

Mild traumatic brain injuries (mTBI) are a worrisome aspect of participation in most sports due to difficulties in their diagnosis in competitions and the potential of long-term neurological defects. These types of injuries are not well understood for athletes playing soccer, specifically pertaining to the risks of heading a soccer ball. Studies are warranted which investigate impacts in this game to improve current knowledge. Our computational study uses finite element modeling to investigate contact between a player's head and the soccer ball. The results of this study present potential injury mechanisms and risks caused by this contact interaction to contribute to the current understanding of brain injuries in soccer and the promotion of athlete safety.

The human brain develops from a smooth cortical surface in early stages of fetal life to a convoluted one postnatally, creating an organized ensemble of folds. Abnormal folding patterns are linked to neurodevelopmental disorders. However, the complex multi-scale interactions involved in cortical folding are not fully known yet. Computational models of brain development have contributed to better understand the process of cortical folding, but still leave several questions unanswered. A major limitation of the existing models is that they have basically been applied to synthetic examples or simplified brain anatomies. However, the integration of patient-specific longitudinal imaging data is key for improving the realism of simulations. In this work we present a complete computational pipeline to build and validate patient-specific mechanical models of brain development. Starting from the processing of fetal brain magnetic resonance images (MRI), personalised finite-element 3D meshes were generated, in which biomechanical models were run to simulate brain development. Several metrics were then employed to compare simulation results with neonatal images from the same subjects, on a common reference space. We applied the computational pipeline to a cohort of 29 subjects where fetal and neonatal MRI were available, including controls and ventriculomegaly cases. The neonatal brain simulations had several sulcal patterns similar to the ones observed in neonatal MRI data. However, the pipeline also revealed some limitations of the evaluated mechanical model and the importance of including patient-specific cortical thickness as well as regional and anisotropic growth to obtain more realistic and personalised brain development models.

Statement of Significance: Computational modelling has emerged as a powerful tool to study the complex process of brain development during gestation. However, most of the studies performed so far have been carried out in synthetic or two-dimensional geometries due to the difficulties involved in processing real fetal data. Moreover, as there is no correspondence between meshes, comparing them or assessing whether they are realistic or not is not a trivial task. In this work we present a complete computational pipeline to build and validate patient-specific mechanical models of brain development, mainly based on open-source tools.

During protein synthesis, aminoacyl-tRNA synthetases covalently link amino acids with their cognate tRNAs. Amino acid mutations in glycyl-tRNA synthetase can disrupt protein synthesis and lead to a neurological disorder known as Charcot-Marie-Tooth disease type 2D (CMT-2D). Several studies employing diverse techniques have identified potential disease mechanisms at the molecular level. The majority of CMT-2D mutations in glycyl-tRNA are found within its dimer interface. However, no atomic structures bearing these mutations have been solved. Consequently, the specific disease-causing structural changes that occur in glycyl-tRNA synthetase have not been definitively established. Here we use molecular dynamics simulations to probe conformational changes in glycyl-tRNA synthetase caused by one mutation within the dimer interface: G240R. Our results show that the mutation alters the number of native interactions at the dimer interface and also leads to altered dynamics of two regions of glycyl-tRNA synthetase associated with tRNA binding. Additionally, we use our simulations to make predictions about the effects of other clinically reported CMT-2D mutations. Our results identify a region of the glycyl-tRNA synthetase structure that may be disrupted in a large number of CMT-2D mutations. Structural changes in this region may be a common molecular mechanism in glycyl-tRNA synthetase CMT-2D pathologies.

Statement of significance

In this study, we use molecular dynamics simulations to elucidate structural conformations accessible to glycyl-tRNA synthetase (GlyRS), an enzyme that ligates cytosolic glycine with tRNA-Gly. This protein contains multiple flexible regions with dynamics that elude in vitro structural characterization. Our computational approach provides unparalleled atomistic details of structural changes in GlyRS that contribute to its role in protein synthesis. A number of mutations in GlyRS are associated with a peripheral nerve disorder, Charcot-Marie-Tooth disease type 2D (CMT-2D). Mutation-induced structural and dynamic changes in GlyRS have similarity that elude in vitro structural characterization. Our simulations provide insights into disease mechanisms for one such mutation: G240R. Additionally, we leverage our computational data to identify regions of GlyRS critical to its function and to predict the effects of other disease-associated mutations. These results open up new directions for research into the molecular characterization of GlyRS and into hypothesis-driven studies of CMT-2D disease mechanisms.