Structural Anisotropy vs. Mechanical Anisotropy: The Contribution of Axonal Fibers to the Material Properties of Brain White Matter

2020 ◽  
Author(s):  
Faezeh Eskandari ◽  
Mehdi Shafieian ◽  
Mohammad M. Aghdam ◽  
Kaveh Laksari
Keyword(s):  
White Matter ◽  
Material Properties ◽  
Mechanical Anisotropy ◽  
Structural Anisotropy ◽  
Brain White Matter

scholarly journals Hyperelastic Material Properties of Axonal fibers in Brain White Matter

2021 ◽  
pp. 100035
Author(s):  
Poorya Chavoshnejad ◽  
Guy K. German ◽  
Mir Jalil Razavi
Keyword(s):  
White Matter ◽  
Material Properties ◽  
Hyperelastic Material ◽  
Brain White Matter

Deep 3D Convolution Neural Network Methods for Brain White Matter Hybrid Computational Simulations

2020 ◽  
Author(s):  
Xuehai Wu ◽  
Assimina A. Pelegri
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
White Matter ◽  
Finite Elements ◽  
Frequency Domain ◽  
Finite Element Methods ◽  
Material Properties ◽  
Output Data ◽  
Micro Scale ◽  
Brain White Matter

Abstract Material properties of brain white matter (BWM) show high anisotropy due to the complicated internal three-dimensional microstructure and variant interaction between heterogeneous brain-tissue (axon, myelin, and glia). From our previous study, finite element methods were used to merge micro-scale Representative Volume Elements (RVE) with orthotropic frequency domain viscoelasticity to an integral macro-scale BWM. Quantification of the micro-scale RVE with anisotropic frequency domain viscoelasticity is the core challenge in this study. The RVE behavior is expressed by a viscoelastic constitutive material model, in which the frequency-related viscoelastic properties are imparted as storage modulus and loss modulus for the composite comprised of axonal fibers and extracellular glia. Using finite elements to build RVEs with anisotropic frequency domain viscoelastic material properties is computationally very consuming and resource-draining. Additionally, it is very challenging to build every single RVE using finite elements since the architecture of each RVE is arbitrary in an infinite data set. The architecture information encoded in the voxelized location is employed as input data and is consequently incorporated into a deep 3D convolution neural network (CNN) model that cross-references the RVEs’ material properties (output data). The output data (RVEs’ material properties) is calculated in parallel using an in-house developed finite element method, which models RVE samples of axon-myelin-glia composites. This novel combination of the CNN-RVE method achieved a dramatic reduction in the computation time compared with directly using finite element methods currently present in the literature.

Identification of a Transversely Isotropic Material Model for White Matter in the Brain

2012 ◽  
Author(s):  
Yuan Feng ◽  
Ruth J. Okamoto ◽  
Ravi Namani ◽  
Guy M. Genin ◽  
Philip V. Bayly
Keyword(s):  
Brain Injury ◽  
White Matter ◽  
Mechanical Anisotropy ◽  
Accurate Information ◽  
In Vitro Tests ◽  
Fiber Direction ◽  
Structural Anisotropy ◽  
Axonal Fiber ◽  
The Brain

Axonal fiber tracts in white matter of the brain form anisotropic structures. It is assumed that this structural anisotropy causes mechanical anisotropy, making white matter tissue stiffer along the axonal fiber direction. This, in turn, will affect the mechanical loading of axonal tracts during traumatic brain injury (TBI). The goal of this study is to use a combination of in-vitro tests to characterize the mechanical anisotropy of white matter and compare it to gray matter, which is thought to be structurally and mechanically isotropic. A more complete understanding of the mechanical anisotropy of brain tissue will provide more accurate information for computational simulations of brain injury.

Brain White Matter Model of Orthotropic Viscoelastic Properties in Frequency Domain

2019 ◽  
Author(s):  
Xuehai Wu ◽  
John G. Georgiadis ◽  
Assimina A. Pelegri
Keyword(s):  
Finite Element ◽  
White Matter ◽  
Finite Element Model ◽  
Frequency Domain ◽  
Material Properties ◽  
Viscoelastic Properties ◽  
Diffusion Mri ◽  
Element Model ◽  
Brain White Matter ◽  
Macro Scale

Abstract Finite element analysis is used to study brain axonal injury and develop Brain White Matter (BWM) models while accounting for both the strain magnitude and the strain rate. These models are becoming more sophisticated and complicated due to the complex nature of the BMW composite structure with different material properties for each constituent phase. State-of-the-art studies, focus on employing techniques that combine information about the local axonal directionality in different areas of the brain with diagnostic tools such as Diffusion-Weighted Magnetic Resonance Imaging (Diffusion-MRI). The diffusion-MRI data offers localization and orientation information of axonal tracks which are analyzed in finite element models to simulate virtual loading scenarios. Here, a BMW biphasic material model comprised of axons and neuroglia is considered. The model’s architectural anisotropy represented by a multitude of axonal orientations, that depend on specific brain regions, adds to its complexity. During this effort, we develop a finite element method to merge micro-scale Representative Volume Elements (RVEs) with orthotropic frequency domain viscoelasticity to an integrated macro-scale BWM finite element model, which incorporates local axonal orientation. Previous studies of this group focused on building RVEs that combined different volume fractions of axons and neuroglia and simulating their anisotropic viscoelastic properties. Via the proposed model, we can assign material properties and local architecture on each element based on the information from the orientation of the axonal traces. Consecutively, a BWM finite element model is derived with fully defined both material properties and material orientation. The frequency-domain dynamic response of the BMW model is analyzed to simulate larger scale diagnostic modalities such as MRI and MRE.

scholarly journals QOL-09. WHOLE-BRAIN WHITE MATTER NETWORK CONNECTIVITY IS DISRUPTED BY PEDIATRIC BRAIN TUMOR TREATMENT

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii432-iii432
Author(s):  
Adeoye Oyefiade ◽  
Kiran Beera ◽  
Iska Moxon-Emre ◽  
Jovanka Skocic ◽  
Ute Bartels ◽  
...  
Keyword(s):  
White Matter ◽  
Pediatric Brain Tumor ◽  
Pediatric Brain Tumors ◽  
Magnetic Resonance Images ◽  
Long Term Effects ◽  
Brain White Matter ◽  
The Right ◽  
Pediatric Brain ◽  
Betweeness Centrality

Abstract INTRODUCTION Treatments for pediatric brain tumors (PBT) are neurotoxic and lead to long-term deficits that are driven by the perturbation of underlying white matter (WM). It is unclear if and how treatment may impair WM connectivity across the entire brain. METHODS Magnetic resonance images from 41 PBT survivors (mean age: 13.19 years, 53% M) and 41 typically developing (TD) children (mean age: 13.32 years, 51% M) were analyzed. Image reconstruction, segmentation, and node parcellation were completed in FreeSurfer. DTI maps and probabilistic streamline generation were completed in MRtrix3. Connectivity matrices were based on the number of streamlines connecting two nodes and the mean DTI (FA) index across streamlines. We used graph theoretical analyses to define structural differences between groups, and random forest (RF) analyses to identify hubs that reliably classify PBT and TD children. RESULTS For survivors treated with radiation, betweeness centrality was greater in the left insular (p < 0.000) but smaller in the right pallidum (p < 0.05). For survivors treated without radiation (surgery-only), betweeness centrality was smaller in the right interparietal sulcus (p < 0.05). RF analyses showed that differences in WM connectivity from the right pallidum to other parts of the brain reliably classified PBT survivors from TD children (classification accuracy = 77%). CONCLUSIONS The left insular, right pallidum, and right inter-parietal sulcus are structurally perturbed hubs in PBT survivors. WM connectivity from the right pallidum is vulnerable to the long-term effects of treatment for PBT.

scholarly journals Pioglitazone Ameliorates Lipopolysaccharide-Induced Behavioral Impairment, Brain Inflammation, White Matter Injury and Mitochondrial Dysfunction in Neonatal Rats

2021 ◽  
Vol 22 (12) ◽  
pp. 6306
Author(s):  
Jiann-Horng Yeh ◽  
Kuo-Ching Wang ◽  
Asuka Kaizaki ◽  
Jonathan W. Lee ◽  
Han-Chi Wei ◽  
...  
Keyword(s):  
Lipid Peroxidation ◽  
White Matter ◽  
Brain Inflammation ◽  
White Matter Injury ◽  
Neonatal Rat ◽  
Mitochondrial Activity ◽  
Neonatal Rats ◽  
Activated Microglia ◽  
Brain White Matter ◽  
Performance Reduction

Previous studies have demonstrated that pioglitazone, a peroxisome proliferator-activated receptor gamma (PPARγ) agonist, inhibits ischemia-induced brain injury. The present study was conducted to examine whether pioglitazone can reduce impairment of behavioral deficits mediated by inflammatory-induced brain white matter injury in neonatal rats. Intraperitoneal (i.p.) injection of lipopolysaccharide (LPS, 2 mg/kg) was administered to Sprague–Dawley rat pups on postnatal day 5 (P5), and i.p. administration of pioglitazone (20 mg/kg) or vehicle was performed 5 min after LPS injection. Sensorimotor behavioral tests were performed 24 h after LPS exposure, and changes in biochemistry of the brain was examined after these tests. The results show that systemic LPS exposure resulted in impaired sensorimotor behavioral performance, reduction of oligodendrocytes and mitochondrial activity, and increases in lipid peroxidation and brain inflammation, as indicated by the increment of interleukin-1β (IL-1β) levels and number of activated microglia in the neonatal rat brain. Pioglitazone treatment significantly improved LPS-induced neurobehavioral and physiological disturbances including the loss of body weight, hypothermia, righting reflex, wire-hanging maneuver, negative geotaxis, and hind-limb suspension in neonatal rats. The neuroprotective effect of pioglitazone against the loss of oligodendrocytes and mitochondrial activity was associated with attenuation of LPS-induced increment of thiobarbituric acid reactive substances (TBARS) content, IL-1β levels and number of activated microglia in neonatal rats. Our results show that pioglitazone prevents neurobehavioral disturbances induced by systemic LPS exposure in neonatal rats, and its neuroprotective effects are associated with its impact on microglial activation, IL-1β induction, lipid peroxidation, oligodendrocyte production and mitochondrial activity.

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