Experimental and Computational Analysis of Brain Deformations in Linear Head Impact

2008 ◽  
Author(s):  
Kurosh Darvish ◽  
Mehdi Shafieian ◽  
Kaveh Laksari ◽  
Banafsheh Barabadi ◽  
Cristina Parenti
Keyword(s):  
Viscoelastic Properties ◽  
Computational Models ◽  
Computational Analysis ◽  
Axonal Injury ◽  
Human Head ◽  
Finite Element Models ◽  
Arbitrary Lagrangian Eulerian ◽  
Strain Stress ◽  
The Brain ◽  
Experimental Strains

In this study two-dimensional physical and finite element models of human head under linear deceleration were developed. 5% gelatin was used as the brain substitute material with similar viscoelastic properties. The experimental strains and pressure during 55G impacts were measured to validate the element formulations used in the computational models. The Lagrangian and Arbitrary Lagrangian Eulerian (ALE) formulations were used in the FE models. It was shown that without Cerebrospinal Fluid (CSF), the Lagrangian strains passed the 10% threshold of axonal injury. At the presence of CSF, no significant strain was observed while 6 to 8 times increase in the intracranial pressure was recorded. The FE models showed similar trends for strain, stress, and pressure but were generally more aggressive than the experimental results. The ALE model was more stable and its effective damping was more consistent with the experimental data.

Influence of Foramen Magnum Boundary Condition on Intracranial Dynamic Response Under Forehead Impact Using Human Body Finite Element Model

2019 ◽  
Vol 17 (07) ◽  
pp. 1950029 ◽  
Author(s):  
Lihai Ren ◽  
Dangdang Wang ◽  
Chengyue Jiang ◽  
Yuanzhi Hu
Keyword(s):  
Boundary Condition ◽  
Finite Element ◽  
Brain Stem ◽  
Axonal Injury ◽  
Human Head ◽  
Foramen Magnum ◽  
Dynamic Responses ◽  
Human Model ◽  
Modeling Methods ◽  
The Brain

The biofidelity is an essential requirement of the application of human head finite element (FE) models to investigate head injuries under mechanical loadings. However, the influence of the foramen magnum boundary condition (FMBC) on intracranial dynamic responses under head impacts has yet to be fully identified until now. This study aimed to investigate the effect of different modeling methods of the FMBC on intracranial dynamic responses induced by forehead impact, especially the axonal injury associated dynamic responses. The total human model for safety (THUMS) was applied in this study. Two FE models with different FMBC modeling methods were developed from the THUMS model. Then, three forehead impact FE models were established respectively, including the original THUMS model. Further FE simulations were conducted to investigate the influence of FMBC modeling methods on intracranial dynamic responses. Though, difference between the intracranial dynamic responses (relative skull-brain motion and strain responses) at areas far from the foramen magnum were slightly, the corresponding difference at the brain stem area were distinctly. Meanwhile, the predicted axonal injury risk of the brain stem white matter was varying among each other. Different modeling methods of FMBC could result in different intracranial dynamic responses of the brain stem, and affect the axonal injury prediction. Therefore, the modeling of the FMBC should be further evaluated for the study of brain stem injury using human head FE models.

In Situ Deformations in the Immature Brain During Rapid Rotations

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Nicole G. Ibrahim ◽  
Rahul Natesh ◽  
Spencer E. Szczesny ◽  
Karen Ryall ◽  
Stephanie A. Eucker ◽  
...  
Keyword(s):  
Computational Models ◽  
Axonal Injury ◽  
Head Rotation ◽  
Lower Velocity ◽  
Brain Deformation ◽  
Injury Mechanisms ◽  
Viscoelastic Modulus ◽  
Lubricated Plate ◽  
The Brain

Head trauma is the leading cause of death and debilitating injury in children. Computational models are important tools used to understand head injury mechanisms but they must be validated with experimental data. In this communication we present in situ measurements of brain deformation during rapid, nonimpact head rotation in juvenile pigs of different ages. These data will be used to validate computational models identifying age-dependent thresholds of axonal injury. Fresh 5 days (n=3) and 4 weeks (n=2) old piglet heads were transected horizontally and secured in a container. The cut surface of each brain was marked and covered with a transparent, lubricated plate that allowed the brain to move freely in the plane of rotation. For each brain, a rapid (20–28 ms) 65 deg rotation was applied sequentially at 50 rad/s, 75 rad/s, and 75 rad/s. Each rotation was digitally captured at 2500 frames/s (480×320 pixels) and mark locations were tracked and used to compute strain using an in-house program in MATLAB. Peak values of principal strain (Epeak) were significantly larger during deceleration than during acceleration of the head rotation (p<0.05), and doubled with a 50% increase in velocity. Epeak was also significantly higher during the second 75 rad/s rotation than during the first 75 rad/s rotation (p<0.0001), suggesting structural alteration at 75 rad/s and the possibility that similar changes may have occurred at 50 rad/s. Analyzing only lower velocity (50 rad/s) rotations, Epeak significantly increased with age (16.5% versus 12.4%, p<0.003), which was likely due to the larger brain mass and smaller viscoelastic modulus of the 4 weeks old pig brain compared with those of the 5 days old. Strain measurement error for the overall methodology was estimated to be 1%. Brain tissue strain during rapid, nonimpact head rotation in the juvenile pig varies significantly with age. The empirical data presented will be used to validate computational model predictions of brain motion under similar loading conditions and to assist in the development of age-specific thresholds for axonal injury. Future studies will examine the brain-skull displacement and will be used to validate brain-skull interactions in computational models.

Mechanical Response of the Brain Under Blast: The Effect of Blast Direction and the Head Protection

2016 ◽  
Author(s):  
Hesam Sarvghad-Moghaddam ◽  
Asghar Rezaei ◽  
Ashkan Eslaminejad ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Brain Injury ◽  
Mechanical Response ◽  
Experimental Studies ◽  
Human Head ◽  
Blast Waves ◽  
Arbitrary Lagrangian Eulerian ◽  
Explosion Pressure ◽  
Head Protection ◽  
Coupling Algorithm ◽  
The Brain

Blast-induced traumatic brain injury (bTBI), is defined as a type of acquired brain injury that occurs upon the interaction of the human head with blast-generated high-pressure shockwaves. Lack of experimental studies due to moral issues, have motivated the researchers to employ computational methods to study the bTBI mechanisms. Accordingly, a nonlinear finite element (FE) analysis was employed to study the interaction of both unprotected and protected head models with explosion pressure waves. The head was exposed to the incoming shockwaves from front, back, and side directions. The main goal was to examine the effects of head protection tools and the direction of blast waves on the tissue and kinematical responses of the brain. Generation, propagation, and interactions of blast waves with the head were modeled using an arbitrary Lagrangian-Eulerian (ALE) method and a fluid-structure interaction (FSI) coupling algorithm. The FE simulations were performed using Ls-Dyna, a transient, nonlinear FE code. Side blast predicted the highest mechanical responses for the brain. Moreover, the protection assemblies showed to significantly alter the blast flow mechanics. Use of faceshield was also observed to be highly effective in the front blast due to hindering of shockwaves.

Changes in Viscoelastic Properties of Brain Tissue Due to Traumatic Injury

2004 ◽  
Author(s):  
Kurosh Darvish ◽  
James Stone
Keyword(s):  
Brain Tissue ◽  
Material Properties ◽  
Computational Models ◽  
Axonal Injury ◽  
Traumatic Injury ◽  
Diffuse Axonal Injury ◽  
Elastic Response ◽  
Tissue Properties ◽  
The Impact ◽  
The Brain

In this study, changes in viscoelastic material properties of brain tissue due to traumatic axonal injury (TAI) were investigated. The impact acceleration model was used to generate diffuse axonal injury in rat brain. TAI in the corticospinal (CSpT) tract in the brain stem was quantified using amyloid precursor protein immunostaining. Material properties along the CSpT were determined using an indentation technique. The results showed that the number of injured axons at the pyramidal decussation (PDx) was approximated 10 times higher than in the ponto-medullary junction (PmJ). The instantaneous elastic response was reduced approximately 70% at PDx compared to 40% at PmJ and the relaxation was uniformly reduced approximately 30%, which were attributed to the effect of injury on tissue properties. Application of a visco-elastic-plastic model that changes due to TAI can significantly alter the results of computational models of brain injury.

scholarly journals Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 229
Author(s):  
JunHyuk Woo ◽  
Hyesun Cho ◽  
YunHee Seol ◽  
Soon Ho Kim ◽  
Chanhyeok Park ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Mitochondrial Dysfunction ◽  
Computational Models ◽  
Neuronal Damage ◽  
Living Organism ◽  
The Body ◽  
Mitochondrial Functions ◽  
A Cell ◽  
The Brain ◽  
And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

Effects of CSF Properties on Brain Response Under Impact Loading

2009 ◽  
Author(s):  
M. S. Chafi ◽  
V. Dirisala ◽  
G. Karami ◽  
M. Ziejewski
Keyword(s):  
Human Head ◽  
Brain Response ◽  
Pia Mater ◽  
Mechanical Shocks ◽  
The Central Nervous System ◽  
Simultaneous Loading ◽  
The Impact ◽  
Impact Experiments ◽  
The Brain

In the central nervous system, the subarachnoid space is the interval between the arachnoid membrane and the pia mater. It is filled with a clear, watery liquid called cerebrospinal fluid (CSF). The CSF buffers the brain against mechanical shocks and creates buoyancy to protect it from the forces of gravity. The relative motion of the brain due to a simultaneous loading is caused because the skull and brain have different densities and the CSF surrounds the brain. The impact experiments are usually carried out on cadavers with no CSF included because of the autolysis. Even in the cadaveric head impact experiments by Hardy et al. [1], where the specimens are repressurized using artificial CSF, this is not known how far this can replicate the real functionality of CSF. With such motivation, a special interest lies on how to model this feature in a finite element (FE) modeling of the human head because it is questionable if one uses in vivo CSF properties (i.e. bulk modulus of 2.19 GPa) to validate a FE human head against cadaveric experimental data.

scholarly journals Aspects of Uncertainty Analysis for Large Nonlinear Computational Models

2010 ◽  
Vol 24-25 ◽  
pp. 25-41 ◽  
Author(s):  
Keith Worden ◽  
W.E. Becker ◽  
Manuela Battipede ◽  
Cecilia Surace
Keyword(s):  
Finite Element ◽  
Monte Carlo ◽  
Computational Models ◽  
Element Model ◽  
Finite Element Models ◽  
Bayesian Algorithm ◽  
Basic Principles ◽  
Structure Interaction ◽  
Output Uncertainty ◽  
Input Uncertainties

This paper concerns the analysis of how uncertainty propagates through large computational models like finite element models. If a model is expensive to run, a Monte Carlo approach based on sampling over the possible model inputs will not be feasible, because the large number of model runs will be prohibitively expensive. Fortunately, an alternative to Monte Carlo is available in the form of the established Bayesian algorithm discussed here; this algorithm can provide information about uncertainty with many less model runs than Monte Carlo requires. The algorithm also provides information regarding sensitivity to the inputs i.e. the extent to which input uncertainties are responsible for output uncertainty. After describing the basic principles of the Bayesian approach, it is illustrated via two case studies: the first concerns a finite element model of a human heart valve and the second, an airship model incorporating fluid structure interaction.

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