Predicting Concussion Symptoms Using Computer Simulations

  • Milan TomaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 880)


The reported rate of concussion is smaller than the actual rate. Less than half of concussion cases in high school football players is reported. The ultimate concern associated with unreported concussion is increased risk of cumulative effects from recurrent injury. This can, partially, be attributed to the fact that the signs and symptoms of a concussion can be subtle and may not show up immediately. Common symptoms after a concussive traumatic brain injury are headache, amnesia and confusion. Computer simulations, based on the impact force magnitude, location and direction, are able to predict these symptoms and their severity. When patients are aware of what to expect in the coming days after head trauma, they are more likely to report the signs of concussion, which decreases the potential risks of unreported injury. In this work, the first ever fluid-structure interaction analysis is used to simulate the interaction between cerebrospinal fluid and comprehensive brain model to assess the concussion symptoms when exposed to head trauma conditions.


Head injury Concussion Fluid-structure interaction Simulations 


  1. 1.
    Goldsmith, W.: Current controversies in the stipulation of head injury criteria - letter to the editor. J. Biomech. 14(12), 883–884 (1981)CrossRefGoogle Scholar
  2. 2.
    Luo, Y., Li, Z., Chen, H.: Finite-element study of cerebrospinal fluid in mitigating closed head injuries. J. Eng. Med. 226(7), 499–509 (2012)CrossRefGoogle Scholar
  3. 3.
    Chafi, M.S., Dirisala, V., Karami, G., Ziejewski, M.: A finite element method parametric study of the dynamic response of the human brain with different cerebrospinal fluid constitutive properties. In: Proceedings of the Institution of Mechanical Engineers, Part H (2009). Journal of Engineering in Medicine 223(8), 1003–1019CrossRefGoogle Scholar
  4. 4.
    Liang, Z., Luo, Y.: A QCT-based nonsegmentation finite element head model for studying traumatic brain injury. Appl. Bionics Biomech. 2015, 1–8 (2015)CrossRefGoogle Scholar
  5. 5.
    Gilchrist, M.D., O’Donoghue, D.: Simulation of the development of the frontal head impact injury. J. Comp. Mech. 26, 229–235 (2000)CrossRefGoogle Scholar
  6. 6.
    Ghajari, M., Hellyer, P.J., Sharp, D.J.: Computational modelling of traumatic brain injury predicts the location of chronic traumatic encephalopathy pathology. Brain 140(2), 333–343 (2017)CrossRefGoogle Scholar
  7. 7.
    McCrea, M., Hammeke, T., Olsen, G., Leo, P., Guskiewicz, K.: Unreported concussion in high school football players: implications for prevention. Clin. J. Sport Med. 14(1), 13–17 (2004)CrossRefGoogle Scholar
  8. 8.
    Rengachary, S.S., Ellenbogen, R.G.: Principles of Neurosurgery. Elsevier Mosby, New York (2005)Google Scholar
  9. 9.
    Toma, M., Nguyen, P.: Fluid-structure interaction analysis of cerebral spinal fluid with a comprehensive head model subject to a car crash-related whiplash. In: 5th International Conference on Computational and Mathematical Biomedical Engineering - CMBE2017. University of Pittsburgh, Pittsburgh (2017)Google Scholar
  10. 10.
    Yanagida, Y., Fujiwara, S., Mizoi, Y.: Differences in the intracranial pressure caused by a blow and/or a fall - experimental study using physical models of the head and neck. Forensic Sci. Int. 41, 135–145 (1989)CrossRefGoogle Scholar
  11. 11.
    Nahum, A.M., Gatts, J.D., Gadd, C.W., Danforth, J.: Impact tolerance of the skull and face. In: 12th Stapp Car Crash Conference, Warrendale, PA, pp. 302–316. Society of Automotive Engineers (1968)Google Scholar
  12. 12.
    Nahum, A.M., Smith, R.W., Ward, C.C.: Intracranial pressure dynamics during head impact. In: 21st Stapp Car Crash Conference (1977)Google Scholar
  13. 13.
    Fry, F.J., Barger, J.E.: Acoustical properties of the human skull. J. Acoust. Soc. Am. 63(5), 1576–1590 (1978)CrossRefGoogle Scholar
  14. 14.
    Barser, T.W., Brockway, J.A., Higgins, L.S.: The density of tissues in and about the head. Acta Neurol. Scandinav. 46, 85–92 (1970)CrossRefGoogle Scholar
  15. 15.
    Elkin, B.S., Azeloglu, E.U., Costa, K.D., Morrison, B.: Mechanical heterogeneity of the rat hippicampus measured by atomic force microscope indentation. J. Neurotrauma 24, 812–822 (2007)CrossRefGoogle Scholar
  16. 16.
    Gefen, A., Gefen, N., Zhu, Q., Raghupathi, R., Margulies, S.S.: Age-dependent changes in material properties of the brain and braincase of the rat. J. Neurotrauma 20, 1163–1177 (2003)CrossRefGoogle Scholar
  17. 17.
    Kruse, S.A., Rose, G.H., Glaser, K.J., Manduca, A., Felmlee, J.P., Jack Jr., C.R., Ehman, R.L.: Magnetic resonance elastography of the brain. Neuroimage 39, 231–237 (2008)CrossRefGoogle Scholar
  18. 18.
    Moore, S.W., Sheetz, M.P.: Biophysics of substrate interaction: influence on neutral motility, differentiation, and repair. Dev. Neurobiol. 71, 1090–1101 (2011)CrossRefGoogle Scholar
  19. 19.
    Lui, A.C., Polis, T.Z., Cicutti, N.J.: Densities of cerebrospinal fluid and spinal anaesthetic solutions in surgical patients at body temperature. Can. J. Anaesth. 45(4), 297–303 (1998)CrossRefGoogle Scholar
  20. 20.
    Toma, M., Einstein, D.R., Bloodworth, C.H., Cochran, R.P., Yoganathan, A.P., Kunzelman, K.S.: Fluid-structure interaction and structural analyses using a comprehensive mitral valve model with 3D chordal structure. Int. J. Numer. Meth. Biomed. Engng. 33(4), e2815 (2017). Scholar
  21. 21.
    Toma, M., Oshima, M., Takagi, S.: Decomposition and parallelization of strongly coupled fluid-structure interaction linear subsystems based on the Q1/P0 discretization. Comput. Struct. 173, 84–94 (2016). Scholar
  22. 22.
    Toma, M.: The emerging use of SPH in biomedical applications. Significances Bioeng. Biosci. 1(1), 1–4 (2017). SBB.000502Google Scholar
  23. 23.
    Brodmann, K.: Vergleichende Lokalisationslehre der Grosshirnrinde (in German). Johann Ambrosius Barth, Leipzig (1909)Google Scholar
  24. 24.
    Limited TCT Research (ed.) Cortical Functions. Trans Cranial Technologies ltd. (2012)Google Scholar
  25. 25.
    Toma, M., Nguyen, P.: Fluid-structure interaction analysis of cerebrospinal fluid with a comprehensive head model subject to a rapid acceleration and deceleration. Brain Inj. 1–9 (2018). Scholar
  26. 26.
    Varlotta, C., Toma, M., Neidecker, J.: Ringside physicians’ medical manual for boxing and mixed martial arts: technology & impact sensor testing. Association of Ringside Physicians, Chapter D10 (2018)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computational Bio-FSI Laboratory, College of Engineering and Computing Sciences, Department of Mechanical EngineeringNew York Institute of TechnologyOld WestburyUSA

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