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A Reanalysis of Experimental Brain Strain Data: Implication for Finite Element Head Model Validation
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
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2019 (English)In: SAE Technical Papers, SAE International , 2019, Vol. 2019, article id NovemberConference paper, Published paper (Refereed)
Abstract [en]

Relative motion between the brain and skull and brain deformation are biomechanics aspects associated with many types of traumatic brain injury (TBI). Thus far, there is only one experimental endeavor (Hardy et al., 2007) reported brain strain under loading conditions commensurate with levels that were capable of producing injury. Most of the existing finite element (FE) head models are validated against brain-skull relative motion and then used for TBI prediction based on strain metrics. However, the suitability of using a model validated against brain-skull relative motion for strain prediction remains to be determined. To partially address the deficiency of experimental brain deformation data, this study revisits the only existing dynamic experimental brain strain data and updates the original calculations, which reflect incremental strain changes. The brain strain is recomputed by imposing the measured motion of neutral density target (NDT) to the NDT triad model. The revised brain strain and the brain-skull relative motion data are then used to test the hypothesis that an FE head model validated against brain-skull relative motion does not guarantee its accuracy in terms of brain strain prediction. To this end, responses of brain strain and brain-skull relative motion of a previously developed FE head model (Kleiven, 2007) are compared with available experimental data. CORrelation and Analysis (CORA) and Normalized Integral Square Error (NISE) are employed to evaluate model validation performance for both brain strain and brain-skull relative motion. Correlation analyses (Pearson coefficient) are conducted between average cluster peak strain and average cluster peak brain-skull relative motion, and also between brain strain validation scores and brain-skull relative motion validation scores. The results show no significant correlations, neither between experimentally acquired peaks nor between computationally determined validation scores. These findings indicate that a head model validated against brain-skull relative motion may not be sufficient to assure its strain prediction accuracy. It is suggested that a FE head model with intended use for strain prediction should be validated against the experimental brain deformation data and not just the brain-skull relative motion.

Place, publisher, year, edition, pages
SAE International , 2019. Vol. 2019, article id November
Series
SAE Technical Papers, ISSN 0148-7191
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:kth:diva-258835DOI: 10.4271/2018-22-0007Scopus ID: 2-s2.0-85065394817OAI: oai:DiVA.org:kth-258835DiVA, id: diva2:1350215
Conference
SAE 62nd Stapp Car Crash Conference, STAPP 2018; Catamaran Resort HotelSan Diego; United States; 12 November 2018 through 14 November 2018
Note

QC 20190911

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-13Bibliographically approved

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