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Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling
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: Stapp Car Crash Journal, ISSN 1532-8546, Vol. 58Article in journal (Refereed) Published
Abstract [en]

Finite element (FE) models are often used to study the biomechanical effects of traumatic brain injury (TBI). Measures based on mechanical responses, such as principal strain or invariants of the strain tensor, are used as a metric to predict the risk of injury. However, the reliability of inferences drawn from these models depends on the correspondence between the mechanical measures and injury data, as well as the establishment of accurate thresholds of tissue injury. In the current study, a validated anisotropic FE model of the human head is used to evaluate the hypothesis that strain in the direction of fibers (axonal strain) is a better predictor of TBI than maximum principal strain (MPS), anisotropic equivalent strain (AESM) and cumulative strain damage measure (CSDM). An analysis of head kinematics-based metrics, such as head injury criterion (HIC) and brain injury criterion (BrIC), is also provided. Logistic regression analysis is employed to compare binary injury data (concussion/no concussion) with continuous strain/kinematics data. The threshold corresponding to 50% of injury probability is determined for each parameter. The predictive power (area under the ROC curve, AUC) is calculated from receiver operating characteristic (ROC) curve analysis. The measure with the highest AUC is considered to be the best predictor of mTBI.Logistic regression shows a statistical correlation between all the mechanical predictors and injury data for different regions of the brain. Peaks of axonal strain have the highest AUC and determine a strain threshold of 0.07 for corpus callosum and 0.15 for the brainstem, in agreement with previously experimentally derived injury thresholds for reversible axonal injury. For a data set of mild TBI from the national football league, the strain in the axonal direction is found to be a better injury predictor than MPS, AESM, CSDM, BrIC and HIC.

Place, publisher, year, edition, pages
2014. Vol. 58
Keyword [en]
Finite element analysis, injury predictor, brain material properties, anisotropy, traumatic brain injury, axonal strain
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-158021Scopus ID: 2-s2.0-84942820205OAI: oai:DiVA.org:kth-158021DiVA: diva2:773435
Note

QC 20150407

Available from: 2014-12-19 Created: 2014-12-19 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

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