On the application of hybrid deep 3D convolutional neural network algorithms for predicting the micromechanics of brain white matter.

Background: Material characterization of brain white matter (BWM) is difficult due to the anisotropy inherent to the three-dimensional microstructure and the various interactions between heterogeneous brain-tissue (axon, myelin, and glia). Developing full scale finite element models that accurately represent the relationship between the micro and macroscale BWM is however extremely challenging and computationally expensive. The anisotropic properties of the microstructure of BWM computed by building unit cells under frequency domain viscoelasticity comprises of 36 individual constants each, for the loss and storage moduli.

An Ogden hyperelastic 3D micromechanical model to depict Poynting effect in brain white matter.

Shear and torsional load on soft solids such as brain white matter purportedly exhibits the Poynting Effect. It is a typical nonlinear phenomenon associated with soft materials whereby they tend to elongate (positive Poynting effect) or contract (negative Poynting effect) in a direction perpendicular to the shearing or twisting plane. In this research, a novel 3D micromechanical Finite Element Model (FEM) has been formulated to describe the Poynting effect in bi-phasic modeled brain white matter (BWM) representative volume element (RVE) with axons tracts embedded in surrounding extracellular matrix (ECM) for simulating brain matter's response to pure and simple shear.

Harmonic viscoelastic response of 3D histology-informed white matter model.

White matter (WM) consists of bundles of long axons embedded in a glial matrix, which lead to anisotropic mechanical properties of brain tissue, and this complicates direct numerical simulations of WM viscoelastic response. The detailed axonal geometry contains scales that range from axonal diameter (microscale) to many diameters (mesoscale) imposing an additional challenge to numerical simulations. Here we describe the development of a 3D homogenization model for the central nervous system (CNS) that accounts for the anisotropy introduced by the axon/neuroglia composite, the axonal trace curvature, and the tissue dynamic response in the frequency domain.

Oligodendrocyte tethering effect on hyperelastic 3D response of axons in white matter.

A novel finite element model is proposed to study the mechanical response of axons embedded in extracellular matrix when subjected to tensile loads under purely non-affine kinematic boundary conditions. Ogden hyperelastic material model describes the axons and the extracellular matrix material characterizations. Two axon-glia tethering scenarios in white matter are studied a single oligodendrocyte (single-OL) with multiple connections a multi-oligodendrocyte (multi-OL) one.

Graphene-reinforced polymer matrix composites fabricated by in situ shear exfoliation of graphite in polymer solution: processing, rheology, microstructure, and properties.

Effective methods are needed to fabricate the next generation of high-performance graphene-reinforced polymer matrix composites (G-PMCs). In this work, a versatile and fundamental process is demonstrated to produce high-quality graphene-polymethylmethacrylate (G-PMMA) composites via in situ shear exfoliation of well-crystallized graphite particles loaded in highly-viscous liquid PMMA/acetone solutions into graphene nanoflakes using a concentric-cylinder shearing device. Unlike other methods where graphene is added externally to the polymer and mixed, our technique is a single step process where as-exfoliated graphene can bond directly with the polymer with no contamination/handling.

Tunable Electrical Properties of Embossed, Cellulose-Based Paper for Skin-like Sensing.

This article describes a process of fabricating highly porous paper from cellulosic fibers and carbon black (CB) with tunable conductivity. By embossing such paper, its porosity decreases while its conductivity increases. Tuning the porosity of composite paper alters the magnitude and trend of conductivity over a spectrum of concentrations of conductive particles.

Sensitivity analysis of effective transverse shear viscoelastic and diffusional properties of myelinated white matter.

Motivated by the need to interpret the results from a combined use of in vivo brain Magnetic Resonance Elastography (MRE) and Diffusion Tensor Imaging (DTI), we developed a computational framework to study the sensitivity of single-frequency MRE and DTI metrics to white matter microstructure and cell-level mechanical and diffusional properties. White matter was modeled as a triphasic unidirectional composite, consisting of parallel cylindrical inclusions (axons) surrounded by sheaths (myelin), and embedded in a matrix (glial cells plus extracellular matrix). Only 2D mechanics and diffusion in the transverse plane (perpendicular to the axon direction) was considered, and homogenized (effective) properties were derived for a periodic domain containing a single axon.

Estimating axonal strain and failure following white matter stretch using contactin-associated protein as a fiduciary marker.

Axonal injury occurs during trauma when tissue-scale loads are transferred to individual axons. Computational models are used to understand this transfer and predict the circumstances that cause injury. However, these findings are limited by a lack of validating experimental work examining the mechanics of axons in their in situ state.

Molecular Dynamics Study of Cubic Boron Nitride Nanoparticles: Decomposition with Phase Segregation during Melting.

The relative stability and melting of cubic boron nitride (c-BN) nanoparticles of varying shapes and sizes are studied using classical molecular dynamics (MD) simulation. Focusing on the melting of octahedral c-BN nanoparticles, which consist solely of the most stable {111} facets, decomposition is observed to occur during melting, along with the formation of phase segregated boron clusters inside the c-BN nanoparticles, concurrent with vaporization of surface nitrogen atoms. To assess this MD prediction, a laser-heating experiment of c-BN powders is conducted, manifesting boron clusters for the post-treated powders.

A Bayesian approach for characterization of soft tissue viscoelasticity in acoustic radiation force imaging.

Biomechanical imaging techniques based on acoustic radiation force (ARF) have been developed to characterize the viscoelasticity of soft tissue by measuring the motion excited by ARF non-invasively. The unknown stress distribution in the region of excitation limits an accurate inverse characterization of soft tissue viscoelasticity, and single degree-of-freedom simplified models have been applied to solve the inverse problem approximately. In this study, the ARF-induced creep imaging is employed to estimate the time constant of a Voigt viscoelastic tissue model, and an inverse finite element (FE) characterization procedure based on a Bayesian formulation is presented.

Characterization of the three-dimensional kinematic behavior of axons in central nervous system white matter.

Traumatic injury to axons in white matter of the brain and spinal cord occurs primarily via tensile stretch. During injury, the stress and strain experienced at the tissue level is transferred to the microscopic axons. How this transfer occurs, and the primary constituents dictating this transfer must be better understood to develop more accurate multi-scale models of injury.

Dynamic simulation of viscoelastic soft tissue in acoustic radiation force creep imaging.

Acoustic radiation force (ARF) creep imaging applies step ARF excitation to induce creep displacement of soft tissue, and the corresponding time-dependent responses are used to estimate soft tissue viscoelasticity or its contrast. Single degree of freedom (SDF) and homogeneous analytical models have been used to characterize soft tissue viscoelasticity in ARF creep imaging. The purpose of this study is to investigate the fundamental limitations of the commonly used SDF and homogeneous assumptions in ARF creep imaging.

Finite Element Modeling of CNS White Matter Kinematics: Use of a 3D RVE to Determine Material Properties.

Axonal injury represents a critical target area for the prevention and treatment of traumatic brain and spinal cord injuries. Finite element (FE) models of the head and/or brain are often used to predict brain injury caused by external mechanical loadings, such as explosive waves and direct impact. The accuracy of these numerical models depends on correctly determining the material properties and on the precise depiction of the tissues' microstructure (microscopic level).

Modelling of global boundary effects on harmonic motion imaging of soft tissues.

Biomechanical imaging techniques have been developed for soft tissue characterisation and detection of breast tumours. Harmonic motion imaging (HMI) uses a focused ultrasound technology to generate a harmonic radiation force in a localised region inside a soft tissue. The resulting dynamic response is used to map the local distribution of the mechanical properties of the tissue.

A transition model for finite element simulation of kinematics of central nervous system white matter.

Mechanical damage to axons is a proximal cause of deficits following traumatic brain injury and spinal cord injury. Axons are injured predominantly by tensile strain, and identifying the strain experienced by axons is a critical step toward injury prevention. White matter demonstrates complex nonlinear mechanical behavior at the continuum level that evolves from even more complex, dynamic, and composite behavior between axons and the "glial matrix" at the microlevel.

Finite element dynamic analysis of soft tissues using state-space model.

A finite element (FE) model is employed to investigate the dynamic response of soft tissues under external excitations, particularly corresponding to the case of harmonic motion imaging. A solid 3D mixed 'u-p' element S8P0 is implemented to capture the near-incompressibility inherent in soft tissues. Two important aspects in structural modelling of these tissues are studied; these are the influence of viscous damping on the dynamic response and, following FE-modelling, a developed state-space formulation that valuates the efficiency of several order reduction methods.

Dynamic simulation of viscoelastic soft tissues in harmonic motion imaging application.

A finite element model was built to simulate the dynamic behavior of soft tissues subjected to sinusoidal excitation during harmonic motion imaging. In this study, soft tissues and tissue-like phantoms were modeled as isotropic, viscoelastic, and nearly incompressible media. A 3D incompressible mixed u-p element of eight nodes, S1P0, was developed to accurately calculate the stiffness matrix for soft tissues.

A mechanical model to compute elastic modulus of tissues for harmonic motion imaging.

Numerous experimental and computational methods have been developed to estimate tissue elasticity. The existing testing techniques are generally classified into in vitro, invasive in vivo and non-invasive in vivo. For each experimental method, a computational scheme is accordingly proposed to calculate mechanical properties of soft biological tissues.