Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
The influence of anisotropy on brain injury prediction
KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.ORCID iD: 0000-0002-0569-5118
KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.ORCID iD: 0000-0003-0125-0784
2014 (English)In: Journal of Biomechanics, ISSN 0021-9290, E-ISSN 1873-2380, Vol. 47, no 5, 1052-1059 p.Article in journal (Refereed) Published
Abstract [en]

Traumatic Brain Injury (TBI) occurs when a mechanical insult produces damage to the brain and disrupts its normal function. Numerical head models are often used as tools to analyze TBIs and to measure injury based on mechanical parameters. However, the reliability of such models depends on the incorporation of an appropriate level of structural detail and accurate representation of the material behavior. Since recent studies have shown that several brain regions are characterized by a marked anisotropy, constitutive equations should account for the orientation-dependence within the brain. Nevertheless, in most of the current models brain tissue is considered as completely isotropic. To study the influence of the anisotropy on the mechanical response of the brain, a head model that incorporates the orientation of neural fibers is used and compared with a fully isotropic model. A simulation of a concussive impact based on a sport accident illustrates that significantly lowered strains in the axonal direction as well as increased maximum principal strains are detected for anisotropic regions of the brain. Thus, the orientation-dependence strongly affects the response of the brain tissue. When anisotropy of the whole brain is taken into account, deformation spreads out and white matter is particularly affected. The introduction of local axonal orientations and fiber distribution into the material model is crucial to reliably address the strains occurring during an impact and should be considered in numerical head models for potentially more accurate predictions of brain injury.

Place, publisher, year, edition, pages
2014. Vol. 47, no 5, 1052-1059 p.
Keyword [en]
Traumatic Brain Injury (TBI), Diffuse Axonal Injury (DAI), Anisotropy, Head model, Finite Element Method (FEM)
National Category
Biophysics
Identifiers
URN: urn:nbn:se:kth:diva-145282DOI: 10.1016/j.jbiomech.2013.12.036ISI: 000334088000017Scopus ID: 2-s2.0-84894686231OAI: oai:DiVA.org:kth-145282DiVA: diva2:717401
Note

QC 20140515

Available from: 2014-05-15 Created: 2014-05-15 Last updated: 2017-05-29Bibliographically approved
In thesis
1. Development of an Anisotropic Finite Element Head Model for Traumatic Brain Injury Prediction
Open this publication in new window or tab >>Development of an Anisotropic Finite Element Head Model for Traumatic Brain Injury Prediction
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traumatic brain injury (TBI) is a worldwide health care problem with very high associatedmorbidity and mortality rates. In particular, the diagnosis of TBI is challenging: symptomsoverlap with other pathologies and the injury is typically not visible with conventionalneuroimaging techniques.Finite element (FE) head models can provide valuable insight into uncovering themechanism underlying brain damage. These models enable the calculation of tissue loadsand deformation patterns, which are thought to be associated with the injury. Measuresbased on tissue strain or invariants of the strain tensor are used as injury predictors and riskinjury curves can be inferred to establish the tolerance of the human head to external loads.However, while in-vitro research shows that the vulnerability to injury is due to highlyorganized structure in white matter tracts, the majority of the current FE models model thebrain as isotropic and homogenous. The deformation of white matter tracts is not calculated.The aim of this doctoral thesis was to incorporate the effects of inhomogeneity andanisotropy of brain tissue into injury analysis. Based on in-vitro experimental evidence, thestrain in the direction of the axons (axonal strain) was proposed as a new, more anatomicallyrelevant, injury predictor. The initial hypothesis to investigate was that an FE anisotropichead model is a better tool to represent TBI because it is more biofidelic in describing thelocal mechanism of axonal impairment.The studies reported in this thesis describe a method for implementing the orientation of thewhite matter tracts in an anisotropic constitutive law for FE modeling. Results from thestudies suggested that the anisotropy of the brain significantly affected the injury predictionsof an FE head model. For an injury dataset from the American National Football League, thepeak of axonal strain - MAS - was found to be a better predictor of injury than isotropic localor global predictors. Finally, based on 27 cases of intracranial pressure, relative skull-brainmotion and brain deformation, the introduction of the brain anisotropy in the FE modelpartially enhanced the biofidelity of the simulations. However, given that the enhancementin biofidelity was not major, it was concluded that further research is necessary forunderstanding the relationship between tissue-level loading and axonal injury.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. 60 p.
Series
TRITA-STH : report, ISSN 1653-3836 ; 2017:5
Keyword
Axonal Strain, Brain Anisotropy, Traumatic Brain Injury, Finite Element Analysis, Brain Tissue, Constitutive Modeling.
National Category
Engineering and Technology
Research subject
Medical Technology
Identifiers
urn:nbn:se:kth:diva-207797 (URN)978-91-7729-357-6 (ISBN)
Public defence
2017-06-02, T2, Hälsovägen 11C, Huddinge, 13:00 (English)
Opponent
Supervisors
Note

QC 20170524

Available from: 2017-05-24 Created: 2017-05-24 Last updated: 2017-05-24Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Giordano, ChiaraKleiven, Svein

Search in DiVA

By author/editor
Giordano, ChiaraKleiven, Svein
By organisation
Neuronic Engineering
In the same journal
Journal of Biomechanics
Biophysics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 144 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf