journal article·2022-08

Investigating the Head Impact Force-Induced Evolution of Hyperphosphorylated Tau Proteins in Brain Tissue Through Mechanical Mesoscale Finite Element Simulation

Ge He, Lei Fan

Acta Mechanica Solida Sinica· Volume 35· Issue 4
Publisher page

Abstract

For human heads that experienced repetitive subconcussive impacts, abnormal accumulation of hyperphosphorylated tau (p-tau) proteins was found in the postmortem brain tissue. To numerically understand the cause–effect relationship between the external force and the microscopic volume change of the p-tau protein, we created a mesoscale finite element model of the multilayer brain tissue containing microscopic voids representing the p-tau proteins. The model was applied under the loading boundary conditions obtained from a larger length scale simulation. A formerly developed internal state variable elastoplasticity model was implemented to describe the constitutive behaviors of gray and white matters, while the cerebrospinal fluid was assumed to be purely elastic. The effects of the initial sizes and distances of p-tau proteins located at four different brain regions (frontal, parietal, temporal and occipital lobes) on their volumetric evolutions were studied. It is concluded that both the initial sizes and distances of the proteins have more or less (depending on the specific brain region) influential effects on the growth or contraction rate of the p-tau protein. The p-tau proteins located within the brain tissue at the frontal and occipital lobes are more heavily affected by the frontal impact load compared with those at the parietal and temporal lobes. In summary, the modeling approach presented in this paper provides a strategy for mechanically studying the evolution of p-tau proteins in the brain tissue and gives insight into understanding the correlation between macroscopic force and microstructure change of the brain tissue.
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Research context

OpenAlex topics: Automotive and Human Injury Biomechanics · Elasticity and Material Modeling · Traumatic Brain Injury Research

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References (22)

Show source-provided references
1. Omalu B. Chronic traumatic encephalopathy. Concussion. 2014;28:38–49. https://doi.org/10.1159/000358761.10.1159/000358761 ↗
2. Stein TD, Alvarez VE, McKee AC. Concussion in chronic traumatic encephalopathy. Curr Pain Headache Rep. 2015;19(10):47. https://doi.org/10.1007/S11916-015-0522-Z.10.1007/S11916-015-0522-Z ↗
3. Stern RA, Riley DO, Daneshvar DH, Nowinski CJ, Cantu RC, McKee AC. Long-term consequences of repetitive brain trauma: chronic traumatic encephalopathy. PM&R. 2011;3(10 SUPPL. 2):S460–7. https://doi.org/10.1016/J.PMRJ.2011.08.008.10.1016/J.PMRJ.2011.08.008 ↗
4. Ac M, et al. The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathol. 2016;131(1):75–86. https://doi.org/10.1007/S00401-015-1515-Z.10.1007/S00401-015-1515-Z ↗
5. McKee AC, Stein TD, Kiernan PT, Alvarez VE. The neuropathology of chronic traumatic encephalopathy. Brain Pathol. 2015;25(3):350. https://doi.org/10.1111/BPA.12248.10.1111/BPA.12248 ↗
6. Puvenna V, et al. Is phosphorylated tau unique to chronic traumatic encephalopathy? Phosphorylated tau in epileptic brain and chronic traumatic encephalopathy. Brain Res. 2016;1630:225–40. https://doi.org/10.1016/J.BRAINRES.2015.11.007.10.1016/J.BRAINRES.2015.11.007 ↗
7. McKee AC, et al. Chronic traumatic encephalopathy in athletes: progressive tauopathy following repetitive head injury. J Neuropathol Exp Neurol. 2009;68(7):709. https://doi.org/10.1097/NEN.0B013E3181A9D503.10.1097/NEN.0B013E3181A9D503 ↗
8. Horstemeyer MF, Berthelson PR, Moore J, Persons AK, Dobbins A, Prabhu RK. A mechanical brain damage framework used to model abnormal brain tau protein accumulations of national football league players. Ann Biomed Eng. 2019;47(9):1873. https://doi.org/10.1007/S10439-019-02294-1.10.1007/S10439-019-02294-1 ↗
9. Horstemeyer MF, Ramaswamy S, Negrete M. Using a micromechanical finite element parametric study to motivate a phenomenological macroscale model for void/crack nucleation in aluminum with a hard second phase. Mech Mater. 2003;35(7):675–87. https://doi.org/10.1016/S0167-6636(02)00165-5.10.1016/S0167-6636(02)00165-5 ↗
10. Lawrimore WB, Francis DK, Bouvard JL, Hammi Y, Horstemeyer MF. A mesomechanics parametric finite element study of damage growth and coalescence in polymers using an Elastoviscoelastic-Viscoplastic internal state variable model. Mech Mater. 2016;96:83–95. https://doi.org/10.1016/J.MECHMAT.2016.02.002.10.1016/J.MECHMAT.2016.02.002 ↗
11. Noël L, Kuhl E. Modeling neurodegeneration in chronic traumatic encephalopathy using gradient damage models. Comput Mech. 2019;64(5):1375–87. https://doi.org/10.1007/S00466-019-01717-Z.10.1007/S00466-019-01717-Z ↗
12. Johnson KL, et al. Constrained topological optimization of a football helmet facemask based on brain response. Mater Des. 2016;111:108–18. https://doi.org/10.1016/J.MATDES.2016.08.064.10.1016/J.MATDES.2016.08.064 ↗
13. Bakhtiarydavijani A, et al. A mesoscale finite element modeling approach for understanding brain morphology and material heterogeneity effects in chronic traumatic encephalopathy, 2021. https://doi.org/10.1080/10255842.2020.1867851.10.1080/10255842.2020.1867851 ↗
14. Crisco JJ, et al. Frequency and location of head impact exposures in individual collegiate football players. J Athl Train. 2010;45(6):549–59. https://doi.org/10.4085/1062-6050-45.6.549.10.4085/1062-6050-45.6.549 ↗
15. He G, Fan L, Liu YC. Mesoscale simulation-based parametric study of damage potential in brain tissue using hyperelastic and internal state variable models. J Biomech Eng. 2021. https://doi.org/10.1115/1.4053205/1129242.10.1115/1.4053205/1129242 ↗
16. Johnson KL, et al. Finite element analysis of a ram brain during impact under wet and dry horn conditions. J Mech Behav Biomed Mater. 2021;119:104400. https://doi.org/10.1016/J.JMBBM.2021.104400.10.1016/J.JMBBM.2021.104400 ↗
17. He G, Liu Y, Deng X, Fan L. Constitutive modeling of viscoelastic-viscoplastic behavior of short fiber reinforced polymers coupled with anisotropic damage and moisture effects. Acta Mech Sin Xuebao. 2019;35(3):495–506. https://doi.org/10.1007/s10409-018-0810-z.10.1007/s10409-018-0810-z ↗
18. He G, Liu Y, Hammi Y, Bammann DJ, Horstemeyer MF. A combined viscoelasticity-viscoplasticity-anisotropic damage model with evolving internal state variables applied to fiber reinforced polymer composites. Mech Adv Mater Struct. 2021;28(17):1775–96. https://doi.org/10.1080/15376494.2019.1709673.10.1080/15376494.2019.1709673 ↗
19. Francis DK, Bouvard JL, Hammi Y, Horstemeyer MF. Formulation of a damage internal state variable model for amorphous glassy polymers. Int J Solids Struct. 2014;51(15–16):2765–76. https://doi.org/10.1016/J.IJSOLSTR.2014.03.025.10.1016/J.IJSOLSTR.2014.03.025 ↗
20. Prabhu RK, et al. Compressive mechanical properties of porcine brain: experimentation and modeling of the tissue hydration effects. Bioengineering. 2019;6(2):40. https://doi.org/10.3390/BIOENGINEERING6020040.10.3390/BIOENGINEERING6020040 ↗
21. Feng Y, Lee CH, Sun L, Ji S, Zhao X. Characterizing white matter tissue in large strain via asymmetric indentation and inverse finite element modeling. J Mech Behav Biomed Mater. 2017;65(178):490–501. https://doi.org/10.1016/j.jmbbm.2016.09.020.10.1016/j.jmbbm.2016.09.020 ↗
22. Prange MT, Margulies SS. Regional, directional, and age-dependent properties of the brain undergoing large deformation. J Biomech Eng. 2002;124(2):244–52. https://doi.org/10.1115/1.1449907.10.1115/1.1449907 ↗