↓ Skip to main content

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

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, January 2013
Altmetric Badge

Mentioned by

twitter
1 X user

Readers on

mendeley
52 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Finite Element Modeling of CNS White Matter Kinematics: Use of a 3D RVE to Determine Material Properties
Published in
Frontiers in Bioengineering and Biotechnology, January 2013
DOI 10.3389/fbioe.2013.00019
Pubmed ID
Authors

Yi Pan, Daniel Sullivan, David I. Shreiber, Assimina A. Pelegri

Abstract

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). Moreover, since the axonal microstructure for specific regions of the brain white matter is locally oriented, the stress, and strain fields are highly anisotropic and axon orientation dependent. Additionally, mechanical strain has been identified as the proximal cause of axonal injury, which further demonstrates the importance of this multi-scale relationship. In this study, our previously developed FE and kinematic axonal models are coupled and applied to a pseudo 3-dimensional representative volume element of central nervous system white matter to investigate the multi-scale mechanical behavior. An inverse FE procedure was developed to identify material parameters of spinal cord white matter by combining the results of uniaxial testing with FE modeling. A satisfactory balance between simulation and experiment was achieved via optimization by minimizing the squared error between the simulated and experimental force-stretch curve. The combination of experimental testing and FE analysis provides a useful analysis tool for soft biological tissues in general, and specifically enables evaluations of the axonal response to tissue-level loading and subsequent predictions of axonal damage.

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 51 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 31%
Researcher 10 19%
Student > Master 8 15%
Student > Doctoral Student 2 4%
Professor 1 2%
Other 2 4%
Unknown 13 25%
Readers by discipline Count As %
Engineering 25 48%
Computer Science 3 6%
Biochemistry, Genetics and Molecular Biology 2 4%
Neuroscience 2 4%
Materials Science 2 4%
Other 3 6%
Unknown 15 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 December 2013.
All research outputs
#20,213,623
of 22,736,112 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#4,562
of 6,505 outputs
Outputs of similar age
#248,822
of 280,780 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#10
of 19 outputs
Altmetric has tracked 22,736,112 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,505 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,780 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.