1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Biomedical Engineering
  4. Volume 22, 2020
  5. Article

Annual Review of Biomedical Engineering

Volume 22, 2020

Mitigating the Consequences of Subconcussive Head Injuries

Abstract

Subconcussive head injury represents a pathophysiology that spans the expertise of both clinical neurology and biomechanical engineering. From both viewpoints, the terms injury and damage, presented without qualifiers, are synonymously taken to mean a tissue alteration that may be recoverable. For clinicians, concussion is evolving from a purely clinical diagnosis to one that requires objective measurement, to be achieved by biomedical engineers. Subconcussive injury is defined as subclinical pathophysiology in which underlying cellular- or tissue-level damage (here, to the brain) is not severe enough to present readily observable symptoms. Our concern is not whether an individual has a (clinically diagnosed) concussion, but rather, how much accumulative damage an individual can tolerate before they will experience long-term deficit(s) in neurological health. This concern leads us to look for the history of damage-inducing events, while evaluating multiple approaches for avoiding injury through reduction or prevention of the associated mechanically induced damage.

Keyword(s): concussion, contact sports, MRI, subconcussive injury, TBI
Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-091219-053447
2020-06-04
2025-05-15
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/22/1/annurev-bioeng-091219-053447.html?itemId=/content/journals/10.1146/annurev-bioeng-091219-053447&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    McKee AC, Cantu RC, Nowinski CJ, Hedley-Whyte ET, Gavett BE et al. 2009. Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury. J. Neuropathol. Exp. Neurol. 68:709–35
     [Google Scholar]
  2. 2. 
    Omalu BI, DeKosky ST, Minster RL, Kamboh MI, Hamilton RL, Wecht CH 2005. Chronic traumatic encephalopathy in a National Football League player. Neurosurgery 57:128–34
     [Google Scholar]
  3. 3. 
    Epstein D. 2010. The damage done. Sports Illus Nov 1
     [Google Scholar]
  4. 4. 
    Talavage TM, Nauman EA, Breedlove EL, Yoruk U, Dye AE et al. 2014. Functionally-detected cognitive impairment in high school football players without clinically-diagnosed concussion. J. Neurotrauma 31:327–38
     [Google Scholar]
  5. 5. 
    Breedlove EL, Robinson M, Talavage TM, Morigaki KE, Yoruk U et al. 2012. Biomechanical correlates of symptomatic and asymptomatic neurophysiological impairment in high school football. J. Biomech. 45:1265–72
     [Google Scholar]
  6. 6. 
    Robinson ME, Shenk TE, Breedlove EL, Leverenz LJ, Nauman EA, Talavage TM 2015. The role of location of subconcussive head impacts in fMRI brain activation change. Dev. Neuropsychol. 40:74–79
     [Google Scholar]
  7. 7. 
    Shenk TE, Robinson ME, Svaldi DO, Abbas K, Breedlove KM et al. 2015. FMRI of visual working memory in high school football players. Dev. Neuropsychol. 40:63–68
     [Google Scholar]
  8. 8. 
    Johnson B, Neuberger T, Gay M, Hallett M, Slobounov S 2014. Effects of subconcussive head trauma on the default mode network of the brain. J. Neurotrauma 31:1907–13
     [Google Scholar]
  9. 9. 
    Abbas K, Shenk TE, Poole VN, Robinson ME, Leverenz LJ et al. 2015. Effects of repetitive sub-concussive brain injury on the functional connectivity of Default Mode Network in high school football athletes. Dev. Neuropsychol. 40:51–56
     [Google Scholar]
  10. 10. 
    Abbas K, Shenk TE, Poole VN, Breedlove EL, Leverenz LJ et al. 2015. Alteration of default mode network in high school football athletes due to repetitive subconcussive mild traumatic brain injury: a resting-state functional magnetic resonance imaging study. Brain Connectivity 5:91–101
     [Google Scholar]
  11. 11. 
    Reynolds BB, Stanton AN, Soldozy S, Goodkin HP, Wintermark M, Druzgal TJ 2018. Investigating the effects of subconcussion on functional connectivity using mass-univariate and multivariate approaches. Brain Imaging Behav 12:1332–45
     [Google Scholar]
  12. 12. 
    Davenport EM, Whitlow CT, Urban JE, Espeland MA, Jung Y et al. 2014. Abnormal white matter integrity related to head impact exposure in a season of high school varsity football. J. Neurotrauma 31:1617–24
     [Google Scholar]
  13. 13. 
    Chun IY, Mao X, Breedlove EL, Leverenz LJ, Nauman EA, Talavage TM 2015. DTI detection of longitudinal WM abnormalities due to accumulated head impacts. Dev. Neuropsychol. 40:92–97
     [Google Scholar]
  14. 14. 
    Bahrami N, Sharma D, Rosenthal S, Davenport EM, Urban JE et al. 2016. Subconcussive head impact exposure and white matter tract changes over a single season of youth football. Radiology 281:919–26
     [Google Scholar]
  15. 15. 
    Davenport EM, Apkarian K, Whitlow CT, Urban JE, Jensen JH et al. 2016. Abnormalities in diffusional kurtosis metrics related to head impact exposure in a season of high school varsity football. J. Neurotrauma 33:2133–46
     [Google Scholar]
  16. 16. 
    Jang I, Chun IY, Brosch JR, Bari S, Zou Y et al. 2019. Every hit matters: White matter diffusivity changes in high school football athletes are correlated with repetitive head acceleration event exposure. NeuroImage Clin 24:101930
     [Google Scholar]
  17. 17. 
    Slobounov SM, Walter A, Breiter HC, Zhu DC, Bai X et al. 2017. The effect of repetitive subconcussive collisions on brain integrity in collegiate football players over a single football season: a multi-modal neuroimaging study. NeuroImage Clin 14:708–18
     [Google Scholar]
  18. 18. 
    Svaldi DO, Joshi C, Robinson ME, Shenk TE, Abbas K et al. 2015. Cerebrovascular reactivity alterations in asymptomatic high school football players. Dev. Neuropsychol. 40:80–84
     [Google Scholar]
  19. 19. 
    Svaldi DO, McCuen EC, Joshi C, Robinson ME, Nho Y et al. 2017. Cerebrovascular reactivity changes in asymptomatic female athletes attributable to high school soccer participation. Brain Imaging Behav 11:98–112
     [Google Scholar]
  20. 20. 
    Svaldi DO, Joshi C, McCuen EC, Music JP, Hannemann R et al. 2020. Accumulation of high magnitude acceleration events predicts cerebrovascular reactivity changes in female high school soccer athletes. Brain Imaging Behav 14:164–74
     [Google Scholar]
  21. 21. 
    Poole VN, Abbas K, Shenk TE, Breedlove EL, Breedlove KM et al. 2014. MR spectroscopic evidence of brain injury in the non-diagnosed collision sport athlete. Dev. Neuropsychol. 39:459–73
     [Google Scholar]
  22. 22. 
    Poole VN, Breedlove EL, Shenk TE, Abbas K, Robinson ME et al. 2015. Sub-concussive hit characteristics predict deviant brain metabolism in football athletes. Dev. Neuropsychol. 40:12–17
     [Google Scholar]
  23. 23. 
    Bari S, Svaldi DO, Jang I, Shenk TE, Poole VN et al. 2019. Dependence on subconcussive impacts of brain metabolism in collision sport athletes: an MR spectroscopic study. Brain Imaging Behav 13:735–49
     [Google Scholar]
  24. 24. 
    McCrory P, Feddermann-Demont N, Dvorak J, Cassidy JD, McIntosh A et al. 2017. What is the definition of sports-related concussion: a systematic review. Br. J. Sports Med. 51:877–87
     [Google Scholar]
  25. 25. 
    Baugh CM, Kroshus E, Daneshvar DH, Stern RA 2014. Perceived coach support and concussion symptom-reporting: differences between freshmen and non-freshmen college football players. J. Law Med. Ethics 42:314–22
     [Google Scholar]
  26. 26. 
    Kerr ZY, Mihalik JP, Guskiewicz KM, Rosamond WD, Evenson KR, Marshall SW 2015. Agreement between athlete-recalled and clinically documented concussion histories in former collegiate athletes. Am. J. Sports Med. 43:606–13
     [Google Scholar]
  27. 27. 
    Kroshus E, Kubzansky LD, Goldman RE, Austin SB 2015. Norms, athletic identity, and concussion symptom under-reporting among male collegiate ice hockey players: a prospective cohort study. Ann. Behav. Med. 49:95–103
     [Google Scholar]
  28. 28. 
    Kroshus E, Garnett B, Hawrilenko M, Baugh CM, Calzo JP 2015. Concussion under-reporting and pressure from coaches, teammates, fans, and parents. Soc. Sci. Med. 134:66–75
     [Google Scholar]
  29. 29. 
    Baugh CM, Kroshus E, Kiernan PT, Mendel D, Meehan WP 3rd 2017. Football players’ perceptions of future risk of concussion and concussion-related health outcomes. J. Neurotrauma 34:790–97
     [Google Scholar]
  30. 30. 
    Kroshus E, Baugh CM, Stein CJ, Austin SB, Calzo JP 2017. Concussion reporting, sex, and conformity to traditional gender norms in young adults. J. Adolesc. 54:110–19
     [Google Scholar]
  31. 31. 
    Kerr ZY, Register-Mihalik JK, Kay MC, DeFreese JD, Marshall SW, Guskiewicz KM 2018. Concussion nondisclosure during professional career among a cohort of former National Football League athletes. Am. J. Sports Med. 46:22–29
     [Google Scholar]
  32. 32. 
    Baugh CM, Kiernan PT, Kroshus E, Daneshvar DH, Montenigro PH et al. 2015. Frequency of head-impact-related outcomes by position in NCAA division I collegiate football players. J. Neurotrauma 32:314–26
     [Google Scholar]
  33. 33. 
    Stamm JM, Koerte IK, Muehlmann M, Pasternak O, Bourlas AP et al. 2015. Age at first exposure to football is associated with altered corpus callosum white matter microstructure in former professional football players. J. Neurotrauma 32:1768–76
     [Google Scholar]
  34. 34. 
    Stamm JM, Bourlas AP, Baugh CM, Fritts NG, Daneshvar DH et al. 2015. Age of first exposure to football and later-life cognitive impairment in former NFL players. Neurology 84:1114–20
     [Google Scholar]
  35. 35. 
    Alosco ML, Kasimis AB, Stamm JM, Chua AS, Baugh CM et al. 2017. Age of first exposure to American football and long-term neuropsychiatric and cognitive outcomes. Transl. Psychiatr. 7:e1236
     [Google Scholar]
  36. 36. 
    Alosco ML, Tripodis Y, Jarnagin J, Baugh CM, Martin B et al. 2017. Repetitive head impact exposure and later-life plasma total tau in former National Football League players. Alzheimers Dement 7:33–40
     [Google Scholar]
  37. 37. 
    Montenigro PH, Alosco ML, Martin BM, Daneshvar DH, Mez J et al. 2017. Cumulative head impact exposure predicts later-life depression, apathy, executive dysfunction, and cognitive impairment in former high school and college football players. J. Neurotrauma 34:328–40
     [Google Scholar]
  38. 38. 
    Alosco ML, Mez J, Tripodis Y, Kiernan PT, Abdolmohammadi B et al. 2018. Age of first exposure to tackle football and chronic traumatic encephalopathy. Ann. Neurol. 83:886–901
     [Google Scholar]
  39. 39. 
    Klein AP, Tetzlaff JE, Bonis JM, Nelson LD, Mayer AR et al. 2019. Prevalence of potentially clinically significant magnetic resonance imaging findings in athletes with and without sport-related concussion. J. Neurotrauma 36:1776–85
     [Google Scholar]
  40. 40. 
    Broglio SP, Sosnoff JJ, Shin S, He X, Alcaraz C, Zimmerman J 2009. Head impacts during high school football: a biomechanical assessment. J. Athl. Train. 44:342–49
     [Google Scholar]
  41. 41. 
    Broglio SP, Eckner JT, Martini D, Sosnoff JJ, Kutcher JS, Randolph C 2011. Cumulative head impact burden in high school football. J. Neurotrauma 28:2069–78
     [Google Scholar]
  42. 42. 
    Broglio SP, Eckner JT, Kutcher JS 2012. Field-based measures of head impacts in high school football athletes. Curr. Opin. Pediatr. 24:702–8
     [Google Scholar]
  43. 43. 
    O'Day KM, Koehling EM, Vollavanh LR, Bradney D, May JM et al. 2017. Comparison of head impact location during games and practices in Division III men's lacrosse players. Clin. Biomech. 43:23–27
     [Google Scholar]
  44. 44. 
    Vollavanh LR, O'Day KM, Koehling EM, May JM, Breedlove KM et al. 2018. Effect of impact mechanism on head accelerations in men's lacrosse athletes. J. Appl. Biomech. 34:396–402
     [Google Scholar]
  45. 45. 
    McCuen E, Svaldi D, Breedlove K, Kraz N, Cummiskey B et al. 2015. Collegiate women's soccer players suffer greater cumulative head impacts than their high school counterparts. J. Biomech. 48:3729–32
     [Google Scholar]
  46. 46. 
    Wilcox BJ, Beckwith JG, Greenwald RM, Chu JJ, McAllister TW et al. 2014. Head impact exposure in male and female collegiate ice hockey players. J. Biomech. 47:109–14
     [Google Scholar]
  47. 47. 
    Brainard LL, Beckwith JG, Chu JJ, Crisco JJ, McAllister TW et al. 2012. Gender differences in head impacts sustained by collegiate ice hockey players. Med. Sci. Sports Exerc. 44:297–304
     [Google Scholar]
  48. 48. 
    Cummiskey B, Schiffmiller D, Talavage TM, Leverenz L, Meyer JJ et al. 2017. Reliability and accuracy of helmet-mounted and head-mounted devices used to measure head accelerations. Proc. Inst. Mechanical Eng. Part P 231:144–53
     [Google Scholar]
  49. 49. 
    Crisco JJ, Chu JJ, Greenwald RM 2004. An algorithm for estimating acceleration magnitude and impact location using multiple nonorthogonal single-axis accelerometers. J. Biomech. Eng. 126:849–54
     [Google Scholar]
  50. 50. 
    Tiernan S, Byrne G, O'Sullivan DM 2019. Evaluation of skin-mounted sensor for head impact measurement. Proc. Inst. Mechanical Eng. Part H 233:735–44
     [Google Scholar]
  51. 51. 
    Bayly PV, Cohen TS, Leister EP, Ajo D, Leuthardt EC, Genin GM 2005. Deformation of the human brain induced by mild acceleration. J. Neurotrauma 22:845–56
     [Google Scholar]
  52. 52. 
    Gomez AD, Xing F, Chan D, Pham DL, Bayly P, Prince JL 2017. Motion estimation with finite-element biomechanical models and tracking constraints from tagged MRI. Computational Biomechanics for Medicine: From Algorithms to Models and Applications A Wittek, GR Joldes, PMF Nielsen, B Doyle & K Miller 81–90 Cham, Switz.: Springer
     [Google Scholar]
  53. 53. 
    Post A, Hoshizaki TB. 2012. Mechanisms of brain impact injuries and their prediction: a review. Trauma 14:327–49
     [Google Scholar]
  54. 54. 
    Oeur RA, Karton C, Post A, Rousseau P, Hoshizaki TB et al. 2015. A comparison of head dynamic response and brain tissue stress and strain using accident reconstructions for concussion, concussion with persistent postconcussive symptoms, and subdural hematoma. J. Neurosurg. 123:415–22
     [Google Scholar]
  55. 55. 
    Ji S, Zhao W, Li Z, McAllister TW 2014. Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation. Biomech. Model. Mechanobiol. 13:1121–36
     [Google Scholar]
  56. 56. 
    Zhao W, Cai Y, Li Z, Ji S 2017. Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter. Biomech. Model. Mechanobiol. 16:1709–27
     [Google Scholar]
  57. 57. 
    Galle B, Ouyang H, Shi R, Nauman E 2007. Correlations between tissue-level stresses and strains and cellular damage within the guinea pig spinal cord white matter. J. Biomech. 40:3029–33
     [Google Scholar]
  58. 58. 
    Galle B, Ouyang H, Shi R, Nauman E 2010. A transversely isotropic constitutive model of excised guinea pig spinal cord white matter. J. Biomech. 43:2839–43
     [Google Scholar]
  59. 59. 
    Zhang W, Zhang RR, Wu F, Feng LL, Yu SB, Wu CW 2016. Differences in the viscoelastic features of white and grey matter in tension. J. Biomech. 49:3990–95
     [Google Scholar]
  60. 60. 
    Ouyang H, Galle B, Li J, Nauman E, Shi R 2008. Biomechanics of spinal cord injury: a multimodal investigation using ex vivo guinea pig spinal cord white matter. J. Neurotrauma 25:19–29
     [Google Scholar]
  61. 61. 
    Ouyang H, Sun W, Fu Y, Li J, Cheng JX et al. 2010. Compression induces acute demyelination and potassium channel exposure in spinal cord. J. Neurotrauma 27:1109–20
     [Google Scholar]
  62. 62. 
    Ouyang H, Nauman E, Shi R 2013. Contribution of cytoskeletal elements to the axonal mechanical properties. J. Biol. Eng. 7:21
     [Google Scholar]
  63. 63. 
    Bar-Kochba E, Scimone MT, Estrada JB, Franck C 2016. Strain and rate-dependent neuronal injury in a 3D in vitro compression model of traumatic brain injury. Sci. Rep. 6:30550
     [Google Scholar]
  64. 64. 
    Smith DH, Wolf JA, Meaney DF 2001. A new strategy to produce sustained growth of central nervous system axons: continuous mechanical tension. Tissue Eng 7:131–39
     [Google Scholar]
  65. 65. 
    LaPlaca MC, Prado GR, Cullen D, Simon CM 2009. Plasma membrane damage as a marker of neuronal injury. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009:1113–16
     [Google Scholar]
  66. 66. 
    Park K, Biederer T. 2013. Neuronal adhesion and synapse organization in recovery after brain injury. Future Neurol 8:555–67
     [Google Scholar]
  67. 67. 
    Zhang L, Bae J, Hardy WN, Monson KL, Manley GT et al. 2002. Computational study of the contribution of the vasculature on the dynamic response of the brain. Stapp Car Crash J 46:145–64
     [Google Scholar]
  68. 68. 
    Monson KL, Goldsmith W, Barbaro NM, Manley GT 2003. Axial mechanical properties of fresh human cerebral blood vessels. J. Biomech. Eng. 125:288–94
     [Google Scholar]
  69. 69. 
    Goldsmith W, Monson KL. 2005. The state of head injury biomechanics: past, present, and future part 2: physical experimentation. Crit. Rev. Biomed. Eng. 33:105–207
     [Google Scholar]
  70. 70. 
    Monson KL, Goldsmith W, Barbaro NM, Manley GT 2005. Significance of source and size in the mechanical response of human cerebral blood vessels. J. Biomech. 38:737–44
     [Google Scholar]
  71. 71. 
    Bell ED, Donato AJ, Monson KL 2017. Cerebrovascular dysfunction following subfailure axial stretch. J. Mech. Behav. Biomed. Mater. 65:627–33
     [Google Scholar]
  72. 72. 
    Schuff MM, Gore JP, Nauman EA 2013. A mixture theory model of fluid and solute transport in the microvasculature of normal and malignant tissues. I. Theory. J. Math. Biol. 66:1179–207
     [Google Scholar]
  73. 73. 
    Schuff MM, Gore JP, Nauman EA 2013. A mixture theory model of fluid and solute transport in the microvasculature of normal and malignant tissues. II: Factor sensitivity analysis, calibration, and validation. J. Math. Biol. 67:1307–37
     [Google Scholar]
  74. 74. 
    Van Harreveld A, Collewijn H, Malhotra SK 1966. Water, electrolytes, and extracellular space in hydrated and dehydrated brains. Am. J. Physiol. 210:251–56
     [Google Scholar]
  75. 75. 
    Krizaj D, Rice ME, Wardle RA, Nicholson C 1996. Water compartmentalization and extracellular tortuosity after osmotic changes in cerebellum of Trachemys scripta. J. . Physiol 492:Part 3887–96
     [Google Scholar]
  76. 76. 
    Weinbaum S, Cowin SC, Zeng Y 1994. A model for the excitation of osteocytes by mechanical loading-induced bone fluid shear stresses. J. Biomech. 27:339–60
     [Google Scholar]
  77. 77. 
    Zeng Y, Cowin SC, Weinbaum S 1994. A fiber matrix model for fluid flow and streaming potentials in the canaliculi of an osteon. Ann. Biomed. Eng. 22:280–92
     [Google Scholar]
  78. 78. 
    Valentin A, Humphrey JD, Holzapfel GA 2013. A finite element-based constrained mixture implementation for arterial growth, remodeling, and adaptation: theory and numerical verification. Int. J. Numer. Methods Biomed. Eng. 29:822–49
     [Google Scholar]
  79. 79. 
    Krishna V, Sammartino F, Rezai A 2018. A review of the current therapies, challenges, and future directions of transcranial focused ultrasound technology: advances in diagnosis and treatment. JAMA Neurol 75:246–54
     [Google Scholar]
  80. 80. 
    Breedlove KM, Breedlove EL, Robinson M, Poole VN, King JR III et al. 2014. Detecting neurocognitive and neurophysiological changes as a result of subconcussive blows in high school football athletes. Athletic Train. Sports Healthc. 6:119–27
     [Google Scholar]
  81. 81. 
    Nauman EA, Breedlove KM, Breedlove EL, Talavage TM, Robinson ME, Leverenz LJ 2015. Post-season neurophysiological deficits assessed by ImPACT and fMRI in athletes competing in American football. Dev. Neuropsychol. 40:85–91
     [Google Scholar]
  82. 82. 
    Gregory S. 2015. Why we need hit counts in football. Time Oct. 12. Updated Oct. 18, 2015
    [Google Scholar]
  83. 83. 
    Lee TA, Lycke RJ, Lee PJ, Cudal CM, Torolski KJ et al. 2020. Distribution of head acceleration events varies by position and play type in North American football. Clin. J. Sport Med. In press. https://doi.org/10.1097/jsm.0000000000000778
    [Crossref] [Google Scholar]
  84. 84. 
    Auerbach PS, Waggoner WH 2nd 2016. It's time to change the rules. JAMA 316:1260–61
     [Google Scholar]
  85. 85. 
    Auerbach PS. 2018. How to save football players’ brains: One simple rule change would solve much of the problem: require the linemen to stand up. Wall Street J. Jan. 19
     [Google Scholar]
  86. 86. 
    Shanley E, Thigpen C, Kissenberth M, Gilliland RG, Thorpe J et al. 2017. Heads up football training decreases concussion rates in high school football players. Clin. J. Sport Med. 2017. https://doi.org/10.1177/2325967117S00131
    [Crossref] [Google Scholar]
  87. 87. 
    Wagner L. 2015. Dartmouth football's brilliant dummies. The Two-Way NPR. https://www.npr.org/sections/thetwo-way/2015/08/28/435542788/dartmouth-footballs-brilliant-dummies
    [Google Scholar]
  88. 88. 
    Jadischke R, Viano DC, Dau N, King AI, McCarthy J 2013. On the accuracy of the Head Impact Telemetry (HIT) System used in football helmets. J. Biomech. 46:2310–15
     [Google Scholar]
  89. 89. 
    Wu LC, Nangia V, Bui K, Hammoor B, Kurt M et al. 2016. In vivo evaluation of wearable head impact sensors. Ann. Biomed. Eng. 44:1234–45
     [Google Scholar]
  90. 90. 
    King AI, Yang KH, Zhang L, Hardy W, Viano DC 2003. Is head injury caused by linear or angular acceleration?. Lecture presented at IRCOBI Conference Lisbon, Portugal: Sep 25–26
  91. 91. 
    Boden BP, Kirkendall DT, Garrett WE Jr. 1998. Concussion incidence in elite college soccer players. Am. J. Sports Med. 26:238–41
     [Google Scholar]
  92. 92. 
    Kontos AP, Elbin RJ, Fazio-Sumrock VC, Burkhart S, Swindell H et al. 2013. Incidence of sports-related concussion among youth football players aged 8–12 years. J. Pediatr. 163:717–20
     [Google Scholar]
  93. 93. 
    Barnes BC, Cooper L, Kirkendall DT, McDermott TP, Jordan BD, Garrett WE Jr 1998. Concussion history in elite male and female soccer players. Am. J. Sports Med. 26:433–38
     [Google Scholar]
  94. 94. 
    Breedlove KM, Breedlove EL, Bowman TG, Arruda EM, Nauman EA 2018. The effect of football helmet facemasks on impact behavior during linear drop tests. J. Biomech. 79:227–31
     [Google Scholar]
  95. 95. 
    Breedlove KM, Breedlove EL, Bowman TG, Nauman EA 2016. Impact attenuation capabilities of football and lacrosse helmets. J. Biomech. 49:2838–44
     [Google Scholar]
  96. 96. 
    Bowman TG, Breedlove KM, Breedlove EL, Dodge TM, Nauman EA 2015. Impact attenuation properties of new and used lacrosse helmets. J. Biomech. 48:3782–87
     [Google Scholar]
  97. 97. 
    Breedlove KM, Breedlove E, Nauman E, Bowman TG, Lininger MR 2017. The ability of an aftermarket helmet add-on device to reduce impact-force accelerations during drop tests. J. Athl. Train. 52:802–8
     [Google Scholar]
  98. 98. 
    Cummiskey B, Sankaran GN, McIver KG, Shyu D, Markel J et al. 2019. Quantitative evaluation of impact attenuation by football helmets using a modal impulse hammer. Proc. Inst. Mechanical Eng. Part P 233:301–11
     [Google Scholar]
  99. 99. 
    McIver KG, Sankaran GN, Lee P, Bucherl S, Leiva N et al. 2019. Impact attenuation of male and female lacrosse helmets using a modal impulse hammer. J. Biomech. 95:109313
     [Google Scholar]
  100. 100. 
    Guskiewicz KM, Register-Mihalik J, McCrory P, McCrea M, Johnston K et al. 2013. Evidence-based approach to revising the SCAT2: introducing the SCAT3. Br. J. Sports Med. 47:289–93
     [Google Scholar]
  101. 101. 
    Drake AI, McDonald EC, Magnus NE, Gray N, Gottshall K 2006. Utility of Glasgow Coma Scale-Extended in symptom prediction following mild traumatic brain injury. Brain Inj 20:469–75
     [Google Scholar]
  102. 102. 
    Nell V, Yates DW, Kruger J 2000. An extended Glasgow Coma Scale (GCS-E) with enhanced sensitivity to mild brain injury. Arch. Phys. Med. Rehabil. 81:614–17
     [Google Scholar]
  103. 103. 
    Randolph C, Millis S, Barr WB, McCrea M, Guskiewicz KM et al. 2009. Concussion symptom inventory: an empirically derived scale for monitoring resolution of symptoms following sport-related concussion. Arch. Clin. Neuropsychol. 24:219–29
     [Google Scholar]
  104. 104. 
    Chin EY, Nelson LD, Barr WB, McCrory P, McCrea MA 2016. Reliability and validity of the Sport Concussion Assessment Tool-3 (SCAT3) in high school and collegiate athletes. Am. J. Sports Med. 44:2276–85
     [Google Scholar]
  105. 105. 
    Lovell MR, Collins MW. 1998. Neuropsychological assessment of the college football player. J. Head Trauma Rehabil. 13:9–26
     [Google Scholar]
  106. 106. 
    Collins MW, Grindel SH, Lovell MR, Dede DE, Moser DJ et al. 1999. Relationship between concussion and neuropsychological performance in college football players. JAMA 282:964–70
     [Google Scholar]
  107. 107. 
    Gross AG. 1958. A new theory on the dynamics of brain concussion and brain injury. J. Neurosurg. 15:548–61
     [Google Scholar]
  108. 108. 
    Hughes DG, Jackson A, Mason DL, Berry E, Hollis S, Yates DW 2004. Abnormalities on magnetic resonance imaging seen acutely following mild traumatic brain injury: correlation with neuropsychological tests and delayed recovery. Neuroradiology 46:550–58
     [Google Scholar]
  109. 109. 
    Talavage TM, Nauman EA, Leverenz LJ 2015. The role of medical imaging in the recharacterization of mild traumatic brain injury using youth sports as a laboratory. Front. Neurol. 6:273
     [Google Scholar]
  110. 110. 
    Moser RS, Schatz P, Lichtenstein JD 2015. The importance of proper administration and interpretation of neuropsychological baseline and postconcussion computerized testing. Appl. Neuropsychol. Child 4:41–48
     [Google Scholar]
  111. 111. 
    Lichtenstein JD, Moser RS, Schatz P 2014. Age and test setting affect the prevalence of invalid baseline scores on neurocognitive tests. Am. J. Sports Med. 42:479–84
     [Google Scholar]
  112. 112. 
    Alsalaheen B, Stockdale K, Pechumer D, Broglio SP 2016. Validity of the Immediate Post Concussion Assessment and Cognitive Testing (ImPACT). Sports Med 46:1487–501
     [Google Scholar]
  113. 113. 
    Iverson GL, Kaarto ML, Koehle MS 2008. Normative data for the balance error scoring system: implications for brain injury evaluations. Brain Inj 22:147–52
     [Google Scholar]
  114. 114. 
    Iverson GL, Koehle MS. 2013. Normative data for the modified balance error scoring system in adults. Brain Inj 27:596–99
     [Google Scholar]
  115. 115. 
    Guskiewicz KM. 2011. Balance assessment in the management of sport-related concussion. Clin. Sports Med. 30:89–102
     [Google Scholar]
  116. 116. 
    Simon M, Maerlender A, Metzger K, Decoster L, Hollingworth A, Valovich McLeod T 2017. Reliability and concurrent validity of select C3 logix test components. Dev. Neuropsychol. 42:446–59
     [Google Scholar]
  117. 117. 
    Hwang S, Ma L, Kawata K, Tierney R, Jeka JJ 2017. Vestibular dysfunction after subconcussive head impact. J. Neurotrauma 34:8–15
     [Google Scholar]
  118. 118. 
    Fischer TD, Red SD, Chuang AZ, Jones EB, McCarthy JJ et al. 2016. Detection of subtle cognitive changes after mTBI using a novel tablet-based task. J. Neurotrauma 33:1237–46
     [Google Scholar]
  119. 119. 
    Kawata K, Rubin LH, Lee JH, Sim T, Takahagi M et al. 2016. Association of football subconcussive head impacts with ocular near point of convergence. JAMA Ophthalmol 134:763–69
     [Google Scholar]
  120. 120. 
    Kawata K, Tierney R, Phillips J, Jeka JJ 2016. Effect of repetitive sub-concussive head impacts on ocular near point of convergence. Int. J. Sports Med. 37:405–10
     [Google Scholar]
  121. 121. 
    Teel EF, Gay MR, Arnett PA, Slobounov SM 2015. Differential sensitivity between a virtual reality balance module and clinically used concussion balance modalities. Clin. J. Sport Med. 26:162–66
     [Google Scholar]
  122. 122. 
    Teel EF, Slobounov SM. 2015. Validation of a virtual reality balance module for use in clinical concussion assessment and management. Clin. J. Sport Med. 25:144–48
     [Google Scholar]
  123. 123. 
    Yorke AM, Smith L, Babcock M, Alsalaheen B 2017. Validity and reliability of the vestibular/ocular motor screening and associations with common concussion screening tools. Sports Health 9:174–80
     [Google Scholar]
  124. 124. 
    Allen BJ, Gfeller JD. 2011. The Immediate Post-Concussion Assessment and Cognitive Testing battery and traditional neuropsychological measures: a construct and concurrent validity study. Brain Inj 25:179–91
     [Google Scholar]
  125. 125. 
    Maerlender A, Flashman L, Kessler A, Kumbhani S, Greenwald R et al. 2010. Examination of the construct validity of ImPACT computerized test, traditional, and experimental neuropsychological measures. Clin. Neuropsychol. 24:1309–25
     [Google Scholar]
  126. 126. 
    Maerlender A, Flashman L, Kessler A, Kumbhani S, Greenwald R et al. 2013. Discriminant construct validity of ImPACT: a companion study. Clin. Neuropsychol. 27:290–99
     [Google Scholar]
  127. 127. 
    Mihalik JP, Lengas E, Register-Mihalik JK, Oyama S, Begalle RL, Guskiewicz KM 2013. The effects of sleep quality and sleep quantity on concussion baseline assessment. Clin. J. Sport Med. 23:343–48
     [Google Scholar]
  128. 128. 
    Trinidad KJ, Schmidt JD, Register-Mihalik JK, Groff D, Goto S, Guskiewicz KM 2013. Predicting clinical concussion measures at baseline based on motivation and academic profile. Clin. J. Sport Med. 23:462–69
     [Google Scholar]
  129. 129. 
    Higgins KL, Caze T, Maerlender A 2018. Validity and reliability of baseline testing in a standardized environment. Arch. Clin. Neuropsychol. 33:437–43
     [Google Scholar]
  130. 130. 
    Maerlender A, Molfese DL. 2015. Repeat baseline assessment in college-age athletes. Dev. Neuropsychol. 40:69–73
     [Google Scholar]
  131. 131. 
    Kellar D, Newman S, Pestilli F, Cheng H, Port NL 2018. Comparing fMRI activation during smooth pursuit eye movements among contact sport athletes, non-contact sport athletes, and non-athletes. NeuroImage Clin 18:413–24
     [Google Scholar]
  132. 132. 
    Johnson B, Hallett M, Slobounov S 2015. Follow-up evaluation of oculomotor performance with fMRI in the subacute phase of concussion. Neurology 85:1163–66
     [Google Scholar]
  133. 133. 
    Koerte IK, Nichols E, Tripodis Y, Schultz V, Lehner S et al. 2017. Impaired cognitive performance in youth athletes exposed to repetitive head impacts. J. Neurotrauma 34:2389–95
     [Google Scholar]
  134. 134. 
    Prichep LS, Ghosh Dastidar S, Jacquin A, Koppes W, Miller J et al. 2014. Classification algorithms for the identification of structural injury in TBI using brain electrical activity. Comput. Biol. Med. 53:125–33
     [Google Scholar]
  135. 135. 
    Brooks MA, Bazarian JJ, Prichep LS, Dastidar SG, Talavage TM, Barr W 2018. The use of an electrophysiological brain function index in the evaluation of concussed athletes. J. Head Trauma Rehabil. 33:1–6
     [Google Scholar]
  136. 136. 
    Hanley D, Prichep LS, Badjatia N, Bazarian J, Chiacchierini R et al. 2018. A brain electrical activity electroencephalographic-based biomarker of functional impairment in traumatic brain injury: a multi-site validation trial. J. Neurotrauma 35:41–47
     [Google Scholar]
  137. 137. 
    Duff J. 2004. The usefulness of quantitative EEG (QEEG) and neurotherapy in the assessment and treatment of post-concussion syndrome. Clin. EEG Neurosci. 35:198–209
     [Google Scholar]
  138. 138. 
    Urban KJ, Barlow KM, Jimenez JJ, Goodyear BG, Dunn JF 2015. Functional near-infrared spectroscopy reveals reduced interhemispheric cortical communication after pediatric concussion. J. Neurotrauma 32:833–40
     [Google Scholar]
  139. 139. 
    Hocke LM, Duszynski CC, Debert CT, Dleikan D, Dunn JF 2018. Reduced functional connectivity in adults with persistent post-concussion symptoms: a functional near-infrared spectroscopy study. J. Neurotrauma 35:1224–32
     [Google Scholar]
  140. 140. 
    Forcione M, Colonnese C, Belli A 2018. Cerebral hemodynamic influences in task-related functional magnetic resonance imaging and near-infrared spectroscopy in acute sport-related concussion: a review. J. Imaging 4:59
     [Google Scholar]
  141. 141. 
    Schneider DK, Galloway R, Bazarian JJ, Diekfuss JA, Dudley J et al. 2019. Diffusion tensor imaging in athletes sustaining repetitive head impacts: a systematic review of prospective studies. J. Neurotrauma 36:2831–49
     [Google Scholar]
/content/journals/10.1146/annurev-bioeng-091219-053447
Loading
Mitigating the Consequences of Subconcussive Head Injuries
Annual Review of Biomedical Engineering 22, 387 (2020); https://doi.org/10.1146/annurev-bioeng-091219-053447
/content/journals/10.1146/annurev-bioeng-091219-053447
/content/journals/10.1146/annurev-bioeng-091219-053447
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error