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A Study on the Effects of Strain Rates on Characteristics of Brain Tissue

[+] Author Affiliations
Mohammad Hosseini Farid, Ashkan Eslaminejad, Mariusz Ziejewski, Ghodrat Karami

North Dakota State University, Fargo, ND

Paper No. IMECE2017-70356, pp. V003T04A003; 8 pages
  • ASME 2017 International Mechanical Engineering Congress and Exposition
  • Volume 3: Biomedical and Biotechnology Engineering
  • Tampa, Florida, USA, November 3–9, 2017
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5836-3
  • Copyright © 2017 by ASME


Traumatic brain injury (TBI) often happens when the brain tissue undergoes a high rate mechanical load. Although numerous research works have been carried out to study the mechanical characterization of brain matter under quasi-static (strain rate ≤ 100 S−1) loading but a limited amount of experimental studies are available for brain tissue behavior under dynamic strain rates (strain rate ≥ 100 S−1). In this paper, the results of a study on mechanical properties of ovine brain tissue under unconfined compression tests are to be presented. The samples were compressed under uniaxial strain rates of 0.0667, 3.33, 6.667, 33.33, 66.667 and 200 S−1. The brain tissue presents a stiffer response with increasing strain rate, showing a time-dependent behavior. So the hyperelastic-only models are not adequate to exhibit the brain viscoelasticity. Therefore, two hyper-viscoelastic constitutive equations based on power function model and Mooney-Rivlin energy function are applied to the results with quasi-static strain rate (≤ 100 S−1). Good agreement of experimental and theoretical has been achieved for results of the low strain rates. It is concluded that the obtained material parameters from quasi-static tests are not appropriate enough to fit the result with the high strain rate of 200 S−1. The study will further provide new insight into a better understanding of the rate-dependency behavior of the brain tissue under dynamic conditions. This is essential in the development of constitutive material characteristics for an efficient human brain finite element models to predict TBI under impact condition or high motion.

Copyright © 2017 by ASME



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