Beyond Linear Elastic Modulus: Viscoelastic Models for Brain and Brain Mimetic Hydrogels
Source abstract
Abstract
With their high degree of specificity and investigator control, in vitro disease models provide a natural complement to in vivo models. Especially in organs such as the brain, where anatomical limitations make in vivo experiments challenging, in vitro models have been increasingly used to mimic disease pathology. However, brain mimetic models may not fully replicate the mechanical environment in vivo, which has been shown to influence a variety of cell behaviors. Specifically, many disease models consider only the linear elastic modulus of brain, which describes the stiffness of a material with the assumption that mechanical behavior is independent of loading rate. Here, we characterized porcine brain tissue using a modified stress relaxation test, and across a panel of viscoelastic models, showed that stiffness depends on loading rate. As such, the linear elastic modulus does not accurately reflect the viscoelastic properties of native brain. Among viscoelastic models, the Maxwell model was selected for further analysis because of its simplicity and excellent curve fit (R2 = 0.99 ± 0.0006). Thus, mechanical response of native brain and hydrogel mimetic models was analyzed using the Maxwell model and the linear elastic model to evaluate the effects of strain rate, time post mortem, region, tissue type (i.e., bulk brain vs white matter), and in brain mimetic models, hydrogel composition, on observed mechanical properties. In comparing the Maxwell and linear elastic models, linear elastic modulus is consistently lower than the Maxwell elastic modulus across all brain regions. Additionally, the Maxwell model is sensitive to changes in viscosity and small changes in elasticity, demonstrating improved fidelity. These findings demonstrate the insufficiency of linear elastic modulus as a primary mechanical characterization for brain mimetic materials and provide quantitative information toward the future design of materials that more closely mimic mechanical features of brain.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Cellular Mechanics and Interactions · Elasticity and Material Modeling · Automotive and Human Injury Biomechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, Iowa State University, Ames, Iowa 50011, United States
Funding
Division of Graduate Education · Division of Civil, Mechanical and Manufacturing Innovation · National Institutes of Health · National Science Foundation · Pelotonia Cancer Research Fellowship
References (38)
Show source-provided references
1. 10.1016/j.cub.2013.01.00910.1016/j.cub.2013.01.009 ↗
2. 10.1098/rsif.2011.056710.1098/rsif.2011.0567 ↗
3. 10.1158/0008-5472.CAN-08-485910.1158/0008-5472.CAN-08-4859 ↗
4. 10.1091/mbc.e11-06-053710.1091/mbc.e11-06-0537 ↗
5. 10.1016/j.cell.2006.06.04410.1016/j.cell.2006.06.044 ↗
6. 10.1371/journal.pone.014581310.1371/journal.pone.0145813 ↗
7. 10.1016/S0006-3495(00)76481-210.1016/S0006-3495(00)76481-2 ↗
8. 10.1371/journal.pone.003257210.1371/journal.pone.0032572 ↗
9. 10.1115/1.161524710.1115/1.1615247 ↗
10. 10.1042/BST035065610.1042/BST0350656 ↗
11. 10.1021/am402097j10.1021/am402097j ↗
12. 10.1016/j.biomaterials.2013.03.06910.1016/j.biomaterials.2013.03.069 ↗
13. 10.1007/s00418-017-1604-210.1007/s00418-017-1604-2 ↗
14. 10.1016/j.biomaterials.2009.10.04710.1016/j.biomaterials.2009.10.047 ↗
15. 10.1007/978-94-007-7602-9_2610.1007/978-94-007-7602-9_26 ↗
16. 10.1038/srep2491210.1038/srep24912 ↗
17. Bentil, S. A Fractional Zener Constitutive Model to Describe the Degradation of Swine Cerebrum with Validation from Experimental Data and Predictions using Finite Element Analysis. Electronic Thesis or Dissertation; The Ohio State University, OhioLINK Electronic Theses and Dissertations Center, 2013.
18. 10.1137/S105262349630347010.1137/S1052623496303470 ↗
19. 10.1016/j.ccr.2005.08.01010.1016/j.ccr.2005.08.010 ↗
20. 10.1016/S0021-9290(99)00010-X10.1016/S0021-9290(99)00010-X ↗
21. 10.1016/j.neuroimage.2007.08.03010.1016/j.neuroimage.2007.08.030 ↗
22. 10.1016/j.jmbbm.2015.02.02410.1016/j.jmbbm.2015.02.024 ↗
23. 10.1097/00001756-200212030-0004010.1097/00001756-200212030-00040 ↗
24. 10.1146/annurev-bioeng-07151604461910.1146/annurev-bioeng-071516044619 ↗
25. 10.1016/j.addr.2012.11.00410.1016/j.addr.2012.11.004 ↗
26. 10.1023/B:MTDM.0000027681.86865.4a10.1023/B:MTDM.0000027681.86865.4a ↗
27. 10.1115/1.164552810.1115/1.1645528 ↗
28. 10.1016/j.jmbbm.2013.10.02010.1016/j.jmbbm.2013.10.020 ↗
29. 10.2307/123388110.2307/1233881 ↗
30. 10.1371/journal.pone.008166810.1371/journal.pone.0081668 ↗
31. 10.1093/brain/40.1.1510.1093/brain/40.1.15 ↗
32. 10.1177/194173811558874510.1177/1941738115588745 ↗
33. Cardarelli R. · Am. Fam. Physician · 2010 · 82 · 1372
34. Purves, D. Neuroscience, 4th ed. Sinauer: Sunderland, MA, 2008; p xvii, 857, G-16, IC-7, I-29.
35. 10.1016/j.biomaterials.2013.06.02410.1016/j.biomaterials.2013.06.024 ↗
36. 10.1073/pnas.94.25.1366110.1073/pnas.94.25.13661 ↗
37. 10.1126/science.111699510.1126/science.1116995 ↗
38. 10.1021/am402097j10.1021/am402097j ↗