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Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.ORCID iD: 0000-0002-0569-5118
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.ORCID iD: 0000-0003-0125-0784
2014 (English)In: 58th SAE Stapp Car Crash Conference, STAPP 2014Article 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
SAE International , 2014.
Keywords [en]
Anisotropy, Axonal strain, Brain material properties, Finite element analysis, Injury predictor, Traumatic brain injury, Accidents, Brain, Bromine compounds, Damage detection, Hybrid integrated circuits, Regression analysis, Risk assessment, Area under the ROC curve, Logistic regression analysis, Maximum principal strain, Mild traumatic brain injuries, Receiver operating characteristic curve analysis, Statistical correlation, Traumatic Brain Injuries, Finite element method
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-246647DOI: 10.4271/2014-22-0002Scopus ID: 2-s2.0-85059447238OAI: oai:DiVA.org:kth-246647DiVA, id: diva2:1327158
Conference
10 November 2014 through 12 November 2014
Note

QC 20190619

Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically approved

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Giordano, ChiaraKleiven, Svein

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