Mechanical properties of brain tissue in tension

This paper contains experimental results of in vitro, uniaxial tension of swine brain tissue in finite deformation as well as proposes a new hyper-viscoelastic constitutive model for the brain tissue. The experimental results obtained for two loading velocities, corresponding to strain rates of 0.64 and 0.64 x 10(-2)s(-1), are presented. We believe that these are the first ever experiments of this kind. The applied strain rates were similar to those applied in our previous study, focused on explaining brain tissue properties in compression. The stress-strain curves are convex downward for all extension rates. The tissue response stiffened as the loading speed increased, indicating a strong stress-strain rate dependence. Swine brain tissue was found to be considerably softer in extension than in compression. Previously proposed in the literature brain tissue constitutive models, developed based on experimental data collected in compression are shown to be inadequate to explain tissue behaviour in tension. A new, non-linear, viscoelastic model based on the generalisation of the Ogden strain energy hyper-elastic constitutive equation is proposed. The new model accounts well for brain tissue deformation behaviour in both tension and compression (natural strain in <-0.3,0.2>) for strain rates ranging over five orders of magnitude.     

Teaching about Scientific Origins: Taking Account of Creationism

The purpose of this study was to develop a model for measuring experimental design ability based on functional magnetic resonance imaging (fMRI) during biological inquiry. More specifically, the researchers developed an experimental design task that measures experimental design ability. Using the developed experimental design task, they measured both the paper experimental design ability and the fMRI experimental design ability of subjects. Subjects’ paper experimental design ability was measured using the quotient equation of experimental design ability, and their fMRI experimental design ability using the brain connectivity coefficient. According to the fMRI results, differences in design ability existed among subjects in terms of brain connectivity coefficient level during the experimental design task. The experimental design ability brain connectivity coefficient level and quotient for each subject were analysed. Statistically significant correlations between subjects’ connectivity strength level among brain activation regions and quotient value guided the establishment of a measuring model. The model measured experimental design ability and could predict an individual’s experimental design ability quotient using his or her brain connectivity coefficient. Hence, the model developed for this study for measuring experimental design ability based on fMRI may serve as a practical measurement of students’ scientific experimental design ability. Furthermore, this study could serve as a founding theory for measuring models of other scientific processing abilities such as observation, question generation, classification, hypothesis generation and hypothesis evaluation.     

Attention and memory model based on the principle of dominant and comparative function of the hippocampus

Six basic problems of attention are described in terms of the dominant focus by Ukhtomsky, which is simulated as a system of phase transitions in the brain. Theoretical and experimental arguments in favor of the existence of metastable states in the brain with the life time of about a second or more are deduced. This forms an approach to resolution of all the problems of interaction between attention and memory, binding, and central control. A neurobiological model of attention and memory is advanced, which integrates the system properties of dominanta by A.A. Ukhtomsky and comparator function of the hippocampus by O.S. Vinogradova. New literature evidence is given for the existence of the brain system of information processing with the hippocampus as a central executive.     

Biomechanical analysis of fluid percussion model of brain injury

Fluid percussion injury (FPI) is a widely used experimental model for studying traumatic brain injury (TBI). However, little is known about how the brain mechanically responds to fluid impacts and how the mechanical pressures/strains of the brain correlate to subsequent brain damage for rodents during FPI. Hence, we developed a numerical approach to simulate FPI experiments on rats and characterize rat brain pressure/strain responses at a high resolution. A previous rat brain model was improved with a new hexahedral elements-based skull model and a new cerebrospinal fluid (CSF) layer. We validated the numerical model against experimentally measured pressures from FPI. Our results indicated that brain tissues under FPI experienced high pressures, which were slightly lower (10–20%) than input saline pressure. Interestingly, FPI was a mixed focus- and diffuse-type injury model with highest strains (12%) being concentrated in the ipsilateral cortex under the fluid-impact site and diffuse strains (5–10%) being spread to the entire brain, which was different from controlled cortical impact in which high strains decreased gradually away from the impact site.     

Mechanical properties of brain tissue in-vivo: experiment and computer simulation

Realistic computer simulation of neurosurgical procedures requires incorporation of the mechanical properties of brain tissue in the mathematical model. Possible applications of computer simulation of neurosurgery include non-rigid registration, virtual reality training and operation planning systems and robotic devices to perform minimally invasive brain surgery. A number of constitutive models of brain tissue, both single-phase and bi-phasic, have been proposed in recent years. The major deficiency of most of them, however, is the fact that they were identified using experimental data obtained in vitro and there is no certainty whether they can be applied in the realistic in vivo setting. In this paper we attempt to show that previously proposed by us hyper-viscoelastic constitutive model of brain tissue can be applied to simulating surgical procedures. An in vivo indentation experiment is described. The force-displacement curve for the loading speed typical for surgical procedures is concave upward containing no linear portion from which a meaningful elastic modulus might be determined. In order to properly analyse experimental data, a three-dimensional, non-linear finite element model of the brain was developed. Magnetic resonance imaging techniques were used to obtain geometric information needed for the model. The shape of the force-displacement curve obtained using the numerical solution was very similar to the experimental one. The predicted forces were about 31% lower than those recorded during the experiment. Having in mind that the coefficients in the model had been identified based on experimental data obtained in vitro, and large variability of mechanical properties of biological tissues, such agreement can be considered as very good. By appropriately increasing material parameters describing instantaneous stiffness of the tissue one is able, without changing the structure of the model, to reproduce experimental curve almost perfectly. Numerical studies showed also that the linear, viscoelastic model of brain tissue is not appropriate for the modelling brain tissue deformation even for moderate strains.     

On the computation of substrate levels in perfused tissues

The recent development of experimental techniques to study the isolated perfused heart and brain have renewed interest in the mathematical modeling of capillary-tissue structures. A new analytical representation is developed for the Krogh cylinder model for blood-tissue structures, and a scheme is presented for determining an asymptotic series approximation for this solution. This solution is explored for model parameters of experimental interest, and the contribution and effect of axial diffusion is demonstrated.     

Development and validation of an atlas-based finite element brain model

Traumatic brain injury is a leading cause of disability and injury-related death. To enhance our ability to prevent such injuries, brain response can be studied using validated finite element (FE) models. In the current study, a high-resolution, anatomically accurate FE model was developed from the International Consortium for Brain Mapping brain atlas. Due to wide variation in published brain material parameters, optimal brain properties were identified using a technique called Latin hypercube sampling, which optimized material properties against three experimental cadaver tests to achieve ideal biomechanics. Additionally, falx pretension and thickness were varied in a lateral impact variation. The atlas-based brain model (ABM) was subjected to the boundary conditions from three high-rate experimental cadaver tests with different material parameter combinations. Local displacements, determined experimentally using neutral density targets, were compared to displacements predicted by the ABM at the same locations. Error between the observed and predicted displacements was quantified using CORrelation and Analysis (CORA), an objective signal rating method that evaluates the correlation of two curves. An average CORA score was computed for each variation and maximized to identify the optimal combination of parameters. The strongest relationships between CORA and material parameters were observed for the shear parameters. Using properties obtained through the described multiobjective optimization, the ABM was validated in three impact configurations and shows good agreement with experimental data. The final model developed in this study consists of optimized brain material properties and was validated in three cadaver impacts against local brain displacement data.     

Establishment of a Murine Model for Metastasis of Cytokine-Producing Tumor to the Brain

The A375 cell line, derived from human malignant melanoma, has characteristics of interleukin-6 (IL-6) production. By using this cell line, we have investigated a murine metastasis model of IL-6-producing tumors to the brain by injecting A375 cells directly into the left cardiac ventricle. Nude mice were anesthetized with intraperitoneal injection of pentobarbital sodium. Next, A375 cells suspended in phosphate-buffered saline (PBS) were injected into the left cardiac ventricle of mice. An intracardiac injection of 10⁵ cells developed tumor colonies in the brain after 4 to 6 weeks. Metastatic cells were found in every lobe of the brain. An immunocytochemical study revealed IL-6 production by A375 cells at the metastatic sites in the brain. By the transfection of genes encoding proteins into A375 cells, a novel model of protein expression in the brain in vivo could be constructed. Our system does not require great skill. Our experimental model will facilitate future studies of the local effects of proteins in the brain.     

Obesity and vulnerability of the CNS

The incidence of obesity is increasing worldwide, and is especially pronounced in developed western countries. While the consequences of obesity on metabolic and cardiovascular physiology are well established, epidemiological and experimental data are beginning to establish that the central nervous system (CNS) may also be detrimentally affected by obesity and obesity-induced metabolic dysfunction. In particular, data show that obesity in human populations is associated with cognitive decline and enhanced vulnerability to brain injury, while experimental studies in animal models confirm a profile of heightened vulnerability and decreased cognitive function. This review will describe findings from human and animal studies to summarize current understanding of how obesity affects the brain. Furthermore, studies aimed at identifying key elements of body-brain dialog will be discussed to assess how various metabolic and adipose-related signals could adversely affect the CNS. Overall, data suggest that obesity-induced alterations in metabolism may significantly synergize with age to impair brain function and accelerate age-related diseases of the nervous system. Thus, enhanced understanding of the effects of obesity and obesity-related metabolic dysfunction on the brain are especially critical as increasing numbers of obese individuals approach advanced age.     

Non-invasive measurement of brain damage in a primate model of multiple sclerosis

Early recognition of whether a product has potential as a new therapy for treating multiple sclerosis (MS) relies upon the quality of the animal models used in the preclinical trials. The promising effects of new treatments in rodent models of experimental autoimmune encephalomyelitis (EAE) have rarely been reproduced in patients suffering from MS. EAE in outbred marmoset monkeys, Callithrix jacchus, is a valid new model, and might provide an experimental link between EAE in rodent models and human MS. Using magnetic resonance imaging techniques similar to those used in patients suffering from MS pathological abnormalities in the brain, white matter of the animal can be visualized and quantified. Moreover, NMR spectroscopy, in combination with pattern recognition, offers an advanced uroscopic technique for the identification of biomarkers of inflammatory demyelination.     

Specific Imaging of Inflammation with the 18kDa Translocator Protein Ligand DPA-714 in Animal Models of Epilepsy and Stroke

Inflammation is a pathophysiological hallmark of many diseases of the brain. Specific imaging of cells and molecules that contribute to cerebral inflammation is therefore highly desirable, both for research and in clinical application. The 18 kDa translocator protein (TSPO) has been established as a suitable target for the detection of activated microglia/macrophages. A number of novel TSPO ligands have been developed recently. Here, we evaluated the high affinity TSPO ligand DPA-714 as a marker of brain inflammation in two independent animal models. For the first time, the specificity of radiolabeled DPA-714 for activated microglia/macrophages was studied in a rat model of epilepsy (induced using Kainic acid) and in a mouse model of stroke (transient middle cerebral artery occlusion, tMCAO) using high-resolution autoradiography and immunohistochemistry. Additionally, cold-compound blocking experiments were performed and changes in blood-brain barrier (BBB) permeability were determined. Target-to-background ratios of 2 and 3 were achieved in lesioned vs. unaffected brain tissue in the epilepsy and tMCAO models, respectively. In both models, ligand uptake into the lesion corresponded well with the extent of Ox42- or Iba1-immunoreactive activated microglia/macrophages. In the epilepsy model, ligand uptake was almost completely blocked by pre-injection of DPA-714 and FEDAA1106, another high-affinity TSPO ligand. Ligand uptake was independent of the degree of BBB opening and lesion size in the stroke model. We provide further strong evidence that DPA-714 is a specific ligand to image activated microglia/macrophages in experimental models of brain inflammation.     

A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data

Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.     

Prediction of methane yield at optimum pH for anaerobic digestion of organic fraction of municipal solid waste

A concept of methane yield at optimum pH was advanced and subsequently a mathematical model that simulates the optimal pH of a batch process for anaerobic digestion of organic fraction of municipal solid waste (MSW) was developed and validated. The model was developed on the basis of the microbial growth kinetics and was divided into three processes: hydrolysis of substrates by hydrolytic bacteria, consumption of soluble substrate by acidogenic bacteria, and finally consumption of acetate and methane generated by methanogenic bacteria. Material balance and liquid phase equilibrium chemistry were used in this study. A series of experiments were conducted to validate the model. The model simulation results agreed reasonably with experimental data in different temperatures and total solid (TS) concentrations under uncontrolled pH. A computer circulation program was used to predict the optimal pH in different conditions. Experiments in different temperatures and TS were run under optimal pH which predicted by the model. The model was succeeded in increasing the methane production and the cumulative methane production had an average increment about 35% in optimal pH of different temperatures and TS.     

Multi-type Galton-Watson process as a model for proliferating human tumour cell populations derived from stem cells: estimation of stem cell self-renewal probabilities in human ovarian carcinomas

A mathematical model for proliferation of tumour cell populations is developed. The cell population is assumed to be organized in a hierarchy of decreasing proliferative potential and increasing degree of differentiation. Using some elements of the theory of Multi-type Galton-Watson processes, a method is proposed for the estimation of Psr, the probability of self-renewal of tumour stem cells, from the experimental distribution of clonal unit sizes obtained in cell culture studies. Six data sets from patients with advanced adenocarcinoma of the ovary are used to demonstrate the method. Reasonable estimates are obtained, and the theoretical colony size distributions predicted by the model appear to be in good qualitative agreement with the experimental ones, and lend support to a stem cell model of tumour growth. The possible significance of Psr as a prognostic factor is briefly discussed.     

Standardization of an orthotopic mouse brain tumor model following transplantation of CT-2A astrocytoma cells

Animal models of glial-derived neoplasms are needed to study the biological mechanisms of glioma tumorigenesis and those that sustain the disease state. With the aim to develop and characterize a suitable in vivo experimental mouse model for infiltrating astrocytoma, with predictable and reproducible growth patterns that recapitulate human astrocytoma, this study was undertaken to analyze the long-term course of a syngeneic orthotopically implanted CT-2A mouse astrocytoma in C57BL/6J mice. Intracranial injection of CT-2A cells into caudate-putamen resulted in development of an aggressive tumor showing typical features of human glioblastoma multiforme, sharing close histological, immunohistochemical, proliferative, and metabolic profiles. To simulate metastatic disease to the brain, CT-2A cells were injected through the internal carotid artery. Tumors identical to those obtained by intracranial injection were obtained. Finally, CT-2A cells were re-isolated from experimental brain tumors and transcranially re-injected into the caudate-putamen of healthy mice. These cells generated new tumors that were indistinguishable from the initial ones, suggesting in vivo self-renewal of tumor cells. Small-animal models are essential for testing novel biological therapies directed against relevant molecular targets. In a preliminary study, experimental CT-2A tumors were chronically treated with the small molecule 77427, a gastrin-releasing peptide (GRP) blocker compound that inhibits angiogenesis. Treated animals developed significantly smaller tumors than controls, suggesting an antitumor action for 77427 in glioblastomas. We conclude that the orthotopic CT-2A tumor model, as described herein, is appropriate to explore the mechanisms of glioma development and for preclinical trials of promising drugs.     

Intranasal administration of insulin lowers amyloid-beta levels in rat model of diabetes

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by abnormal accumulation of amyloid beta (A beta) peptide in brain regions subserving memory and other cognitive functions. Hyperglycemia and perturbed insulin signaling have been proposed as pathogenic factors contributing to AD. The aim of the present study is to validate the use of streptozotocin (STZ) injected rats as an experimental model of AD. Using this model, the effect of intranasal administration of insulin on reduction of A beta levels was measured. The current findings strengthen the case for insulin as therapy for AD afflicted individuals with or without diabetes.     

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

     

Multi-Type Galton-Watson Process As A Model For Proliferating Human Tumour Cell Populations Derived From Stem Cells: Estimation of Stem Cell Self-Renewal Probabilities In Human Ovarian Carcinomas

Abstract. A mathematical model for proliferation of tumour cell populations is developed. the cell population is assumed to be organized in a hierarchy of decreasing proliferative potential and increasing degree of differentiation. Using some elements of the theory of Multi-type Galton-Watson processes, a method is proposed for the estimation of P_sr, the probability of self-renewal of tumour stem cells, from the experimental distribution of clonal unit sizes obtained in cell culture studies. Six data sets from patients with advanced adenocarcinorna of the ovary are used to demonstrate the method. Reasonable estimates are obtained, and the theoretical colony size distributions predicted by the model appear to be in good qualitative agreement with the experimental ones, and lend support to a stem cell model of tumour growth. the possible significance of P_sr as a prognostic factor is briefly discussed.     

Encoding brain network response to free viewing of videos

A challenging goal for cognitive neuroscience researchers is to determine how mental representations are mapped onto the patterns of neural activity. To address this problem, functional magnetic resonance imaging (fMRI) researchers have developed a large number of encoding and decoding methods. However, previous studies typically used rather limited stimuli representation, like semantic labels and Wavelet Gabor filters, and largely focused on voxel-based brain patterns. Here, we present a new fMRI encoding model to predict the human brain’s responses to free viewing of video clips which aims to deal with this limitation. In this model, we represent the stimuli using a variety of representative visual features in the computer vision community, which can describe the global color distribution, local shape and spatial information and motion information contained in videos, and apply the functional connectivity to model the brain’s activity pattern evoked by these video clips. Our experimental results demonstrate that brain network responses during free viewing of videos can be robustly and accurately predicted across subjects by using visual features. Our study suggests the feasibility of exploring cognitive neuroscience studies by computational image/video analysis and provides a novel concept of using the brain encoding as a test-bed for evaluating visual feature extraction.     

Model of red blood cell membrane skeleton: electrical and mechanical properties

A theoretical membrane skeleton model of erythrocyte has been developed and successfully applied to interpret electrical and mechanical properties of the red blood cell spectrin-actin network. The model is based on the structure of the membrane skeleton that is comprised of unit cells each containing an actin protofilament and shooting forth a few spectrin heterodimers. The loose ends of the heterodimers of adjacent cells can form bonds with each other giving rise to an integrated network. The number of bonds depends on the temperature. The bond length being excessive (2.6 times the distance between the centers of adjacent cells), the bonds are flexible, and can thus be regarded as entropy springs. The advanced model has been employed to calculate the shear modulus of the membrane skeleton as well as to establish its temperature dependence. In a wide range of temperatures mu(T) is a decreasing function well fitting the experimental data. The relationship between the membrane bilayer-free size of the skeleton and the ionic strength of the solution has been derived to appear in good agreement with the results obtained previously. Experimental data combined with the advanced theory yield the average number of heterodimers per unit cell, m0, as equal to ca. 5; the spectrin heterodimer charge has been estimated.