Abstract
Traumatic brain injury is one of the most prevalent health conditions in the United States. However, despite its significance and frequency there is not that much understanding of the mechanism that controls the brain response during injurious loading. Because brain testing conditions are different between several assessment methods, this is considered as a confounding problem as brain biomechanics cannot be analyzed and understood completely. Multivariate linear regression has been applied in this article as a statistical method to expound the correlations between brain biomechanical response and in vitro brain testing conditions under uniaxial deformation. Neighborhood component analysis has been used to extract ten relevant continuous parameters, namely, age, strain rate, diameter, thickness, length, width, height, storage temperature, testing temperature, and post-mortem preservation time, five different categorical parameters, namely, stress condition, species, specimen location, brain matter composition, and geometry. In addition, multivariate regression model has been estimated with the storage, loss, and complex moduli as the responses. Intercept, strain rate, gray brain matter, and white brain matter have been discovered to be the most consistently significant parameters across the three response variables.
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References
Faul, M., Xu, L., Wald, M.M., Coronado, V.G.: Traumatic brain injury in the united states: emergency department visits, hospitalizations and deaths. In: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Atlanta, GA, pp. 2002–2006 (2010)
Nicolle, S., Lounis, M., Willinger, R.: Shear properties of brain tissue over a frequency range relevant for automotive impact situations: new experimental results. Stapp Car Crash J. 48, 239–258 (2004)
Arani, A., et al.: Measuring the effects of aging and sex on regional brain stiffness with MR elastography in healthy older adults. NeuroImage J. 111, 59–64 (2015)
Chatelin, S., Vappou, J., Roth, S., Raul, J., Willinger, R.: Towards child versus adult brain mechanical properties. J. Mech. Behav. Biomed. Mater. 6, 166–173 (2012)
Hrapko, M., Van Dommelen, J.A.W., Peters, G.W.M., Wismans, J.S.: The influence of test conditions on characterization of the mechanical properties of brain tissue. J. Biomech. Eng. 130, 031003 (2008)
Zhang, J., et al.: Effects of tissue preservation temperature on high strain-rate material properties of brain. J. Biomech. 44, 391–396 (2011)
Garo, A., Hrapko, M., Van Dommelen, J., Peters, G.W.M.: Towards a reliable characterisation of the mechanical behaviour of brain tissue: the effects of post-mortem time and sample preparation. Biorheol. J. 44, 51–58 (2007)
Crawford, F., Abuomar, O., Jones, M., King, R., Prabhu, R.: Data mining the effects of testing conditions on brain biomechanical properties. In: Proceedings of the 2017 International Conference on Data Mining, Las Vegas, NV, USA (2017)
Brands, D.W., Bovendeerd, P.H., Peters, G.W., Wismans, J.S.: The large shear strain dynamic behaviour of in-vitro porcine brain tissue and a silicone gel model material. Stapp Car Crash J. 44, 249–260 (2000)
Forte, A.E., Gentleman, S.M., Dini, D.: On the characterization of the heterogeneous mechanical response of human brain tissue. Biomech. Model. Mechanobiol. 16(3), 907–920 (2016)
Hrapko, M., Van Dommelen, J.A.W., Peters, G.W.M., Wismans, J.S.: The mechanical behaviour of brain tissue: large strain response and constitutive modelling. Biorheol. J. 43(5), 623–636 (2006)
Thibault, K.L., Margulies, S.S.: Material properties of the developing porcine brain. In: Proceedings of the 1996 International IRCOBI Conference on the Biomechanics of Impact, Dublin, Ireland, pp. 75–85 (1996)
Thibault, K.L., Margulies, S.S.: Age-dependent material properties of the porcine cerebrum: effect on pediatric inertial head injury criteria. J. Biomech. 31, 1119–1126 (1998)
Vappou, J., Breton, E., Choquet, P., Goetz, C., Willinger, R., Constantinesco, A.: Magnetic resonance elastography compared with rotational rheometry for in vitro brain tissue viscoelasticity measurement. J. Magn. Reson. Mater. Phys. Biol. Med. 20, 273–278 (2007)
Rohatgi, A.: WebPlotDigitizer (2016). http://arohatgi.info/WebPlotDigitizer/app/
Yang, W., Wang, K., Zuo, W.: Neighborhood component feature selection for high-dimensional data. J. Comput. 7(1), 161–168 (2012)
Beck, N., Katz, J.N.: What to do (and not to do) with time-series-cross-section data in comparative politics. Am. Polit. Sci. Rev. 89(3), 634–647 (1995)
Crawford, F., Fisher, J., Abuomar, O., Prabhu, R.: A multivariate linear regression analysis of in vitro testing conditions and brain biomechanical response under shear loads. In: Proceedings of the 14th International Conference on Data Science (ICDATA 2018), Las Vegas, USA, (2018)
Bilston, L.E., Liu, Z., Phan-Thien, N.: Linear viscoelastic properties of bovine brain tissue in shear. Biorheol. J. 34, 377–385 (1997)
Ozawa, H., Matsumoto, T., Ohashi, T., Sato, M., Kokubun, S.: Comparison of spinal cord gray matter and white matter softness: measurement by pipette aspiration method. J. Neurosurg. 95(2), 221–224 (2001)
Van Dommelen, J.A.W., Van Der Sande, T.P.J., Hrapko, M., Peters, G.W.M.: Mechanical properties of brain tissue by indentation: Interregional variation. J. Mech. Behav. Biomed. Mater. 3, 158–166 (2010)
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Abuomar, O., Patterson, F., Prabhu, R.K. (2020). Regression Analysis of Brain Biomechanics Under Uniaxial Deformation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_11
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