Effects of tissue preservation temperature on high strain-rate material properties of brain

Zhang, Jiangyue; Yoganandan, Narayan; Pintar, Frank A.; Guan, Yabo; Shender, Barry S.; Paskoff, Glenn; Laud, Purushottam W.

doi:10.1016/j.jbiomech.2010.10.024

Cited by 45 publications

(47 citation statements)

References 29 publications

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“…The measured results were found to be clearly temperature dependent and the dynamic modulus * G , at 23 o C, was approximately 35% higher than at 37 o C. Stiffening of the samples occurred with decreasing temperature. Zhang et al (2011) conducted tests on porcine brain tissue at high -strain rates specifically to investigate stress -strain behavior at ice cold temperature and at 37 o C. The estimated stresses at 37 o C were 60 -70% higher than at ice cold temperature, showing a stiffer response of brain tissue at higher temperature (37 o C). These findings are in direct contradiction to the research conducted by Hrapko et al (2008), which showed stiffer response of brain tissue at the lower temperature (23 o C).…”

Section: Discussionmentioning

confidence: 99%

Mechanical characterization of brain tissue in compression at dynamic strain rates

Rashid

Destrade

Gilchrist

2012

Journal of the Mechanical Behavior of Biomedical Materials

278

167

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Traumatic brain injury (TBI) occurs when local mechanical load exceeds certain tolerance levels for brain tissue. Extensive research has been done previously for brain matter experiencing compression at quasistatic loading; however, limited data is available to model TBI under dynamic impact conditions. In this research, an experimental setup was developed to perform unconfined compression tests and stress relaxation tests at strain rates ≤90/s. The brain tissue showed a stiffer response with increasing strain rates, showing that hyperelastic models are not adequate. Specifically, the compressive nominal stress at 30% strain was 8.83 ± 1.94, 12.8 ± 3.10 and 16.0 ± 1.41 kPa (mean ± SD) at strain rates of 30, 60 and 90/s, respectively. Relaxation tests were also conducted at 10%-50% strain with the average rise time of 10 ms, which can be used to derive time dependent parameters. Numerical simulations were performed using one-term Ogden model with initial shear modulus μ(o)=6.06±1.44, 9.44 ± 2.427 and 12.64 ± 1.227 kPa (mean ± SD) at strain rates of 30, 60 and 90/s, respectively. A separate set of bonded and lubricated tests were also performed under the same test conditions to estimate the friction coefficient μ, by adopting combined experimental-computational approach. The values of μ were 0.1 ± 0.03 and 0.15 ± 0.07 (mean ± SD) at 30 and 90/s strain rates, respectively, indicating that pure slip conditions cannot be achieved in unconfined compression tests even under fully lubricated test conditions. The material parameters obtained in this study will help to develop biofidelic human brain finite element models, which can subsequently be used to predict brain injuries under impact conditions.

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Section: Discussionmentioning

confidence: 99%

Mechanical characterization of brain tissue in compression at dynamic strain rates

Rashid

Destrade

Gilchrist

2012

Journal of the Mechanical Behavior of Biomedical Materials

278

167

View full text Add to dashboard Cite

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“…In work motivated by blast-TBI, a key concern is how to measure these material properties under high strain rate conditions. Hopkinson bar-based methods are now scaled to examine shear properties at very high rates, but the soft nature of brain tissue makes this a very challenging set of experiments [83][84][85]. Though values are reported at high strain levels (to 50% engineering strain, an overwhelmingly destructive loading condition), the experiments lack sufficient resolution to estimate the response at more realistic strain levels associated with primary blast injury ($1% strain).…”

Section: An Integrated Multiscale Approach For Understanding Traumatmentioning

confidence: 99%

The Mechanics of Traumatic Brain Injury: A Review of What We Know and What We Need to Know for Reducing Its Societal Burden

Meaney

Morrison

Bass

2014

Journal of Biomechanical Engineering

203

126

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Traumatic brain injury (TBI) is a significant public health problem, on pace to become the third leading cause of death worldwide by 2020. Moreover, emerging evidence linking repeated mild traumatic brain injury to long-term neurodegenerative disorders points out that TBI can be both an acute disorder and a chronic disease. We are at an important transition point in our understanding of TBI, as past work has generated significant advances in better protecting us against some forms of moderate and severe TBI. However, we still lack a clear understanding of how to study milder forms of injury, such as concussion, or new forms of TBI that can occur from primary blast loading. In this review, we highlight the major advances made in understanding the biomechanical basis of TBI. We point out opportunities to generate significant new advances in our understanding of TBI biomechanics, especially as it appears across the molecular, cellular, and whole organ scale.

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“…Experimental data were gathered from several brain tension and compression testing studies Chinzei 1997, 2002;Shen et al 2006;Tamura et al 2008;Chen 2009, 2011;Zhang et al 2011;Rashid et al 2012aRashid et al , 2012bRashid et al , 2012cRashid et al , 2012dRashid et al , 2012e, 2014Li et al 2015Li et al , 2019. The focus of this paper is on uniaxial tension and compression data.…”

Section: Methodsmentioning

confidence: 99%

Data mining the effects of testing conditions and specimen properties on brain biomechanics

Patterson

Abuomar

Jones

et al. 2019

International Biomechanics

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Traumatic brain injury is highly prevalent in the United States. However, despite its frequency and significance, there is little understanding of how the brain responds during injurious loading. A confounding problem is that because testing conditions vary between assessment methods, brain biomechanics cannot be fully understood. Data mining techniques, which are commonly used to determine patterns in large datasets, were applied to discover how changes in testing conditions affect the mechanical response of the brain. Data at various strain rates were collected from published literature and sorted into datasets based on strain rate and tension vs. compression. Self-organizing maps were used to conduct a sensitivity analysis to rank the testing condition parameters by importance. Fuzzy C-means clustering was applied to determine if there were any patterns in the data. The parameter rankings and clustering for each dataset varied, indicating that the strain rate and type of deformation influence the role of these parameters in the datasets. ARTICLE HISTORY

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Effects of tissue preservation temperature on high strain-rate material properties of brain

Cited by 45 publications

References 29 publications

Mechanical characterization of brain tissue in compression at dynamic strain rates

Mechanical characterization of brain tissue in compression at dynamic strain rates

The Mechanics of Traumatic Brain Injury: A Review of What We Know and What We Need to Know for Reducing Its Societal Burden

Data mining the effects of testing conditions and specimen properties on brain biomechanics

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