Annals of Biomedical Engineering

, Volume 40, Issue 1, pp 127–140 | Cite as

Maximum Principal Strain and Strain Rate Associated with Concussion Diagnosis Correlates with Changes in Corpus Callosum White Matter Indices

  • Thomas W. McAllisterEmail author
  • James C. Ford
  • Songbai Ji
  • Jonathan G. Beckwith
  • Laura A. Flashman
  • Keith Paulsen
  • Richard M. Greenwald


On-field monitoring of head impacts, combined with finite element (FE) biomechanical simulation, allow for predictions of regional strain associated with a diagnosed concussion. However, attempts to correlate these predictions with in vivo measures of brain injury have not been published. This article reports an approach to and preliminary results from the correlation of subject-specific FE model-predicted regions of high strain associated with diagnosed concussion and diffusion tensor imaging to assess changes in white matter integrity in the corpus callosum (CC). Ten football and ice hockey players who wore instrumented helmets to record head impacts sustained during play completed high field magnetic resonance imaging preseason and within 10 days of a diagnosed concussion. The Dartmouth Subject-Specific FE Head model was used to generate regional predictions of strain and strain rate following each impact associated with concussion. Maps of change in fractional anisotropy (FA) and median diffusivity (MD) were generated for the CC of each athlete to correlate strain with change in FA and MD. Mean and maximum strain rate correlated with change in FA (Spearman ρ = 0.77, p = 0.01; 0.70, p = 0.031), and there was a similar trend for mean and maximum strain (0.56, p = 0.10; 0.6, p = 0.07), as well as for maximum strain with change in MD (−0.63, p = 0.07). Change in MD correlated with injury-to-imaging interval (ρ = −0.80, p = 0.006) but change in FA did not (ρ = 0.18, p = 0.62). These results provide preliminary confirmation that model-predicted strain and strain rate in the CC correlate with changes in indices of white matter integrity.


Concussion Mild traumatic brain injury Strain FEM brain model Diffusion tensor imaging 



This study was supported through NIH RO1NS055020, R01HD048638, CDC R01/CE001254, the National Operating Committee on Standards for Athletic Equipment (NOCSAE 04-07 & SAC-1), and the William H. Neukom 1964 Institute for Computational Science at Dartmouth College.

Conflict of interest

Richard M. Greenwald, and Simbex have a financial interest in the instruments (HIT System, Sideline Response System (Riddell, Inc)) that were used to collect the biomechanical data reported in this study.


  1. 1.
    Agel, J., R. Dick, B. Nelson, S. W. Marshall, and T. P. Dompier. Descriptive epidemiology of collegiate women’s ice hockey injuries: National Collegiate Athletic Association Injury Surveillance System, 2000–2001 through 2003–2004. J. Athl. Train. Dev. J. 42:249–254, 2007.Google Scholar
  2. 2.
    Alexander, D. C., G. J. Barker, and S. R. Arridge. Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med. 48:331–340, 2002.PubMedCrossRefGoogle Scholar
  3. 3.
    Belanger, H., R. Vanderploeg, G. Curtiss, and D. Warden. Recent neuroimaging techniques in mild traumatic brain injury. J. Neuropsychiatr. Clin. Neurosci. 19:5–20, 2007.CrossRefGoogle Scholar
  4. 4.
    Blumbergs, P. C., G. Scott, J. Manavis, H. Wainwright, D. A. Simpson, and A. J. McLean. Staining of amyloid precursor protein to study axonal damage in mild head injury. Lancet 344:1055–1056, 1994.PubMedCrossRefGoogle Scholar
  5. 5.
    Bonett, D. G., and T. A. Wright. Sample size requirements for estimating Pearson, Kenall, and Spearman correlations. Psychometrika 64:23–28, 2000.CrossRefGoogle Scholar
  6. 6.
    Chu J., J. G. Beckwith, J. J. Crisco, and R. M. Greenwald. A novel algorithm to measure linear and rotational acceleration using single-axis accelerometers. Presented at 5th World Congress of Biomechanics, Munich, Germany, 2006.Google Scholar
  7. 7.
    Chu, Z., E. A. Wilde, J. V. Hunter, S. R. McCauley, E. D. Bigler, et al. Voxel-based analysis of diffusion tensor imaging in mild traumatic brain injury in adolescents. AJNR 31:340–346, 2010.PubMedCrossRefGoogle Scholar
  8. 8.
    Crisco, J. J., R. Fiore, J. G. Beckwith, J. J. Chu, P. G. Brolinson, et al. Frequency and location of head impact exposures in individual collegiate football players. J. Athl. Train. 45:549–559, 2010.PubMedCrossRefGoogle Scholar
  9. 9.
    Cubon, V. A., M. Putukian, C. Boyer, and A. Dettwiler. A diffusion tensor imaging study on the white matter skeleton in individuals with sports related concussion. J. Neurotrauma 28:189–201, 2011.PubMedCrossRefGoogle Scholar
  10. 10.
    DeKosky, S. T., M. D. Ikonomovic, and S. Gandy. Traumatic brain injury—football, warfare, and long-term effects. N. Engl. J. Med. 363:1293–1296, 2010.PubMedCrossRefGoogle Scholar
  11. 11.
    Dick, R., M. S. Ferrara, J. Agel, et al. Descriptive epidemiology of collegiate men’s football injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 through 2003–2004. J. Athl. Train. 42:221–233, 2007.PubMedGoogle Scholar
  12. 12.
    Duhaime, A.-C. Large animal models of traumatic injury to the immature brain. Dev. Neurosci. 28:380–387, 2006.PubMedCrossRefGoogle Scholar
  13. 13.
    Duma, S., S. Manoogian, W. Bussone, P. Brolinson, M. Goforth, et al. Analysis of real-time head accelerations in collegiate football players. Clin. J. Sports Med. 15:3–8, 2005.CrossRefGoogle Scholar
  14. 14.
    Farkas, O., and J. T. Povlishock. Cellular and subcellular change evoked by diffuse traumatic brain injury: a COMPLEX web of change extending far beyond focal damage. Prog. Brain Res. 161:43–59, 2007.PubMedCrossRefGoogle Scholar
  15. 15.
    Farrell, J. A. D., B. A. Landman, C. K. Jones, S. A. Smith, J. L. Prince, et al. Effects of SNR on the accuracy and reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T. J. Magn. Reson. Imaging 26:756–767, 2007.PubMedCrossRefGoogle Scholar
  16. 16.
    Feng, Y., T. M. Abney, R. J. Okamoto, R. B. Pless, G. M. Genin, and P. V. Bayly. Relative brain displacement and deformation during constrained mild frontal head impact. J. R. Soc. Interface 7:1677–1688, 2010.PubMedCrossRefGoogle Scholar
  17. 17.
    Fischl, B., D. H. Salat, E. Busa, M. Albert, M. Dieterich, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 33:341–355, 2002.PubMedCrossRefGoogle Scholar
  18. 18.
    Fischl, B., D. H. Salat, A. J. W. van der Kouwe, N. Markris, F. Ségonne, and A. M. Dale. Sequence-independent segmentation of magnetic resonance images. NeuroImage 23:S69–S84, 2004.PubMedCrossRefGoogle Scholar
  19. 19.
    Franceschini, G., D. Bigoni, P. Regitnig, and G. A. Holzapfel. Brain tissue deforms similarly to filled elastomers and follows consolidation theory. J. Mech. Phys. Solids. 54:2592–2620, 2006.CrossRefGoogle Scholar
  20. 20.
    Franceschini, G. The Mechanics of Human Brain Tissue. PhD-Thesis. University of Trento, 2006.Google Scholar
  21. 21.
    Funk, J. R., S. M. Duma, S. J. Manoogian, and S. Rowson. Biomechanical risk estimates for mild traumatic brain injury. Annu. Proc. Assoc. Adv. Automot. Med. 51:343–361, 2007.PubMedGoogle Scholar
  22. 22.
    Greenwald, R., J. Gwin, J. Chu, and J. Crisco. Head impact severity measures for evaluating mild traumatic brain injury risk exposure. Neurosurgery. 62:789–798, 2008.PubMedCrossRefGoogle Scholar
  23. 23.
    Gwin, J., J. Chu, and R. Greenwald. Head impact telemetry system for measurement of head acceleration in ice hockey. J. Biomech. 39:S153, 2006.CrossRefGoogle Scholar
  24. 24.
    Gwin, J., J. Chu, T. McAllister, and R. Greenwald. In situ measures of head impact acceleration in NCAA division I Men’s Ice Hockey: implications for ASTM F1045 and other ice hockey helmet standards. J. ASTM Int. 6:1–10, 2009.CrossRefGoogle Scholar
  25. 25.
    Han, X., and B. Fischl. Atlas renormalization for improved brain MR image segmentation across scanner platforms. IEEE Trans. Med. Imaging. 26:479–486, 2007.PubMedCrossRefGoogle Scholar
  26. 26.
    Hardy, W. N., M. J. Mason, C. D. Foster, C. S. Shah, J. M. Kopacz, et al. A study of the response of the human cadaver head to impact. Stapp Car Crash J. 51:17–80, 2007.PubMedGoogle Scholar
  27. 27.
    Ji, S., J. Ford, R. Greenwald, et al. Automated subject-specific, hexahedral mesh generation via image registration. Finite Elem. Anal. Des. 47:1178–1185, 2011.PubMedCrossRefGoogle Scholar
  28. 28.
    Ji, S., and S. S. Margulies. Brainstem motion within the skull: measurement of the pons displacement in vivo. J. Biomech. 40:92–99, 2007.PubMedCrossRefGoogle Scholar
  29. 29.
    Ji, S., L. Zhu, L. Dougherty, and S. S. Margulies. In vivo measurements of human brain displacement. Stapp Car Crash J. 48:527–539, 2004.Google Scholar
  30. 30.
    Kleiven, S. Influence of impact direction on the human head in prediction of subdural hematoma. J. Neurotrauma 20:365–379, 2003.PubMedCrossRefGoogle Scholar
  31. 31.
    Kleiven, S. Predictors for traumatic brain injuries evaluated through accident reconstructions. Stapp Car Crash J. 51:81–114, 2007.PubMedGoogle Scholar
  32. 32.
    Kleiven, S., and W. H. Hardy. Correlation of an FE model of the human head with local brain motion—consequences for injury prediction. Stapp Car Crash J. 46:123–144, 2002.PubMedGoogle Scholar
  33. 33.
    Kumar, R., R. Gupta, et al. Comparative evaluation of CC DTI metrics in acute mild and moderate TBI: it’s correlation with np tests. Brain Inj. 23:675–685, 2009.PubMedCrossRefGoogle Scholar
  34. 34.
    Langlois, J., W. Rutland-Brown, and K. Thomas. Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations, and Deaths. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2004.Google Scholar
  35. 35.
    Manoogian, S., D. McNeely, S. Duma, G. Brolinson, and R. Greenwald. Head acceleration is less than 10 percent of helmet acceleration in football impacts. Biomed. Sci. Instrum. 42:383–388, 2006.PubMedGoogle Scholar
  36. 36.
    Mayer, A., J. Lin, M. Mannell, C. Gasparovic, J. Phillips, et al. A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology. 74:643–650, 2010.PubMedCrossRefGoogle Scholar
  37. 37.
    McCrea, M., W. Barr, K. Guskiewicz, C. Randolph, S. Marchall, et al. Standard regression-based methods for measuring recovery after sport-related concussion. J. Int. Neuropsychol. Soc. 11:58–69, 2005.PubMedCrossRefGoogle Scholar
  38. 38.
    McCrea, M., K. M. Guskiewicz, S. W. Marshall, W. B. Barr, C. Randolph, et al. Acute effects and recovery time following concussion in collegiate football players. JAMA. 290:2556–2563, 2003.PubMedCrossRefGoogle Scholar
  39. 39.
    McCrory, P. Sports concussion and the risk of chronic neurological impairment. Clin. J. Sport Med. 21:6–12, 2011.PubMedCrossRefGoogle Scholar
  40. 40.
    McCrory, P., W. Meeuwisse, K. Johnston, J. Dvorak, M. Aubry, et al. Consensus statement on concussion in sport—presented at the 3rd International Conference on Concussion in Sport in Zurich, November 2008. Clin. J. Sport Med. 19:185–200, 2009.PubMedCrossRefGoogle Scholar
  41. 41.
    McKee, A. C., R. C. Cantu, C. J. Nowinski, E. T. Hedley-Whyte, B. E. Gavett, et al. Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury. J. Neuropathol. Exp. Neurol. 68:709–735, 2009.  10.1097/NEN.0b013e3181a9d503.
  42. 42.
    Meehan, W. P., P. d’Hemecourt, and R. Comstock. High school concussions in the 2008–2009 academic year: mechanism, symptoms, and management. Am. J. Sports Med. 38:2405–2409, 2010.PubMedCrossRefGoogle Scholar
  43. 43.
    Miller, R., S. Margulies, M. Leoni, M. Nonaka, X. Chen, et al. Finite element modeling approaches for predicting injury in an experimental model of severe diffuse axonal injury. Proceedings of the 42nd Stapp Car Crash Conference, pp. 155–166, 1998.Google Scholar
  44. 44.
    Mukherjee, P., S. W. Chung, J. I. Berman, C. P. Hess, and R. G. Henry. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am. J. Neuroradiol. 29:843–852, 2008.PubMedCrossRefGoogle Scholar
  45. 45.
    Nahum, A. M., R. Smith, and C. Ward. Intracranial pressure dynamics during head impact. In: Society of Automotive Engineers. Proceedings of 21st Stapp Car Crash Conference, SAE Paper, Warrendale, PA, pp. 337–366, 1977.Google Scholar
  46. 46.
    Nicolle, S., M. Lounis, R. Willinger, and J. F. Palierne. Shear linear behavior of brain tissue over a large frequency range. Biorheology. 42:209–223, 2005.PubMedGoogle Scholar
  47. 47.
    Omalu, B. I., S. T. DeKosky, R. L. Minster, M. I. Kamboh, R. L. Hamilton, and C. H. Wecht. Chronic traumatic encephalopathy in a national football league player. Neurosurg. Clin. N. Am. 57:128–134, 2005.Google Scholar
  48. 48.
    Penumetcha, N., S. Kabadi, B. Jedynak, C. Walcutt, M. H. Gado, et al. Feasibility of geometric-intensity-based semi-automated delineation of the tentorium cerebelli from MRI scans. J. Neuroimaging 21:148–155, 2011.CrossRefGoogle Scholar
  49. 49.
    Powell, J. W., and K. D. Barber-Foss. Traumatic brain injury in high school athletes. JAMA. 182:958–963, 1999.CrossRefGoogle Scholar
  50. 50.
    Rowson, S., J. G. Beckwith, J. J. Chu, D. S. Leonard, R. M. Greenwald, and S. M. Duma. A six degree of freedom head acceleration measurement device for use in football. J. Appl. Biomech. 27:8–14, 2011.PubMedGoogle Scholar
  51. 51.
    Rowson, S., and S. M. Duma. Development of the star evaluation system for football helmets: integrating player head impact exposure and risk of concussion. Ann. Biomed. Eng. 39:2130–2140, 2011.PubMedCrossRefGoogle Scholar
  52. 52.
    Sabet, A. A., E. Christoforou, B. Zatlin, G. M. Genin, and P. V. Bayly. Deformation of the human brain induced by mild angular head acceleration. J. Biomech. 41:307–315, 2008.PubMedCrossRefGoogle Scholar
  53. 53.
    Schwarz, A. Suicide reveals signs of a disease seen in the N.F.L. The New York Times, 2010.Google Scholar
  54. 54.
    Smith, S. M. Fast robust automated brain extraction. Hum. Brain Mapp. 17:143–155, 2002.PubMedCrossRefGoogle Scholar
  55. 55.
    Smith, C. Neuropathology. In: Textbook of Traumatic Brain Injury, edited by J. Silver, T. McAllister, and S. Yudofsky. Washington, DC: American Psychiatric Publishing, 2011.Google Scholar
  56. 56.
    Smith, S. M., M. Jenkinson, M. W. Woolrich, C. F. Beckmann, T. E. J. Behrens, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 23(Suppl 1):S208–S219, 2004.PubMedCrossRefGoogle Scholar
  57. 57.
    Takhounts, E. G., S. A. Ridella, V. Hasija, R. E. Tannous, J. Q. Campbell, et al. Investigation of traumatic brain injuries using the next generation of stimulated injury monitor (Simon) finite element head model. Stapp Car Crash J. 52:1–31, 2008.PubMedGoogle Scholar
  58. 58.
    Thibault, L., T. Gennarelli, S. Margulies, J. Marcus, and R. Eppinger. The strain dependent pathophysiological consequences of inertial loading on central nervous system tissue. Presented at IRCOBI Conference, Bron, Lyon, France, 1990.Google Scholar
  59. 59.
    Tournier, J. D., S. Mori, and A. Leemans. Diffusion tensor imaging and beyond. Magn. Reson. Med. 65:1532–1556, 2011.PubMedCrossRefGoogle Scholar
  60. 60.
    Trosseille, X., C. Tarriere, F. Lavaste, F. Guillon, and A. Domont. Development of a F.E.M. of the human head according to a specific test protocol. Proceedings of the 36th Stapp Car Crash Conference, Seatttle, Washington, USA, SAE 922527, 1992.Google Scholar
  61. 61.
    Viano, D., I. Casson, E. Pellman, L. Zhang, A. King, and K. Yang. Concussion in professional football: brain responses by finite element analysis: part 9. Neurosurgery. 57:891–916, 2005.PubMedCrossRefGoogle Scholar
  62. 62.
    Wiegell, M. R., H. B. Larsson, and V. J. Wedeen. Fiber crossing in human brain depicted with diffusion tensor MR imaging. Radiology. 217:897–903, 2000.PubMedGoogle Scholar
  63. 63.
    Wu, Z., H. Guo, N. Chow, J. Sallstrom, R. D. Bell, et al. Role of the MEOX2 homeobox gene in neurovascular dysfunction in alzheimer disease [see comment]. Nat. Med. 11:959–965, 2005.PubMedGoogle Scholar
  64. 64.
    Yang, K. H., J. Hu, N. A. White, A. I. King, C. C. Chou, and P. Prasad. Development of numerical models for injury biomechanics research: a review of 50 years of publications in the Stapp Car Crash Conference. Stapp Car Crash J. 50:429–490, 2006.PubMedGoogle Scholar
  65. 65.
    Zhang, H., P. Yushkevich, and J. Gee. DTI toolkit: a spatial normalization and atlas construction toolkit optimized for examining white matter morphometry using DTI data. Poster Presented at the 17th Scientific Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine, 2009.Google Scholar
  66. 66.
    Zhang, L., K. H. Yang, and A. I. King. Comparison of brain responses between frontal and lateral impacts by finite element modeling. J. Neurotrauma. 18:21–30, 2001.PubMedCrossRefGoogle Scholar
  67. 67.
    Zhang, L., K. H. Yang, and A. I. King. A proposed injury threshold for mild traumatic brain injury. J. Biomech. Eng. 126:226–236, 2004.PubMedCrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2011

Authors and Affiliations

  • Thomas W. McAllister
    • 1
    Email author
  • James C. Ford
    • 1
  • Songbai Ji
    • 2
  • Jonathan G. Beckwith
    • 3
  • Laura A. Flashman
    • 1
  • Keith Paulsen
    • 2
  • Richard M. Greenwald
    • 2
    • 3
  1. 1.Department of Psychiatry, Section of NeuropsychiatryDartmouth Medical School, Dartmouth-Hitchcock Medical CenterLebanonUSA
  2. 2.Thayer School of EngineeringDartmouth CollegeHanoverUSA
  3. 3.SimbexLebanonUSA

Personalised recommendations