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Post-Acute Cortical Thickness in Children with Mild Traumatic Brain Injury versus Orthopedic Injury

Ware, Ashley L., Goodrich-Hunsaker, Naomi J., Lebel, Catherine, Shukla, Ayushi, Wilde, Elisabeth A., Abildskov, Tracy J., Bigler, Erin D., Cohen, Daniel M., Mihalov, Leslie K., Bacevice, Ann, Bangert, Barbara A., Taylor, H. Gerry, Yeates, Keith Owen·2020·Journal of Neurotrauma

Abstract

Studies of brain morphometry may illuminate the effects of pediatric mild traumatic brain injury (TBI; e.g., concussion). However, no published studies have examined cortical thickness in the early injury phases of pediatric mild TBI using an appropriate comparison group. The current study used an automated approach (i.e., FreeSurfer) to determine whether cortical thickness differed in children following a mild TBI or a mild orthopedic injury (OI), and to examine whether post-acute cortical thickness predicted post-acute and chronic post-concussive symptoms (PCS). Children ages 8.00-16.99 years with mild TBI ( n = 136) or OI ( n = 70) were recruited at emergency department visits to two children's hospitals, during which parents rated children's pre-injury symptoms retrospectively. Children completed a post-acute (3–24 days post-injury) assessment, which included a 3 Tesla MRI, and 3- and 6-month post-injury assessments. Parents and children rated PCS at each assessment. Cortical thickness was estimated using FreeSurfer. Linear mixed effects and multi-variable negative binomial regression models were used to test study aims, with false discovery rate (FDR) correction for multiple comparisons. Groups differed significantly on left parietal cortical thickness (TBI > OI) after FDR correction. Cortical thickness also varied by brain subregion and age, but not sex. Groups differed significantly on PCS post-acutely (TBI > OI), but not at 3 or 6 months. Right frontal thickness was positively related to post-acute PCS in both groups. Right cingulum thickness predicted chronic PCS in the OI group only. Results highlight the complexity of predicting outcomes of pediatric mild TBI from post-acute neuroimaging biomarkers.

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  1. 10.1093/arclin/acp006 · DOI · Paper page
  2. Gilchrist J., , Thomas K., , Xu L., , McGuire L.C., , and Coronado V.G. (2011). Nonfatal traumatic brain injuries related to sports and recreation activities among persons aged ≤19 years—United States, 2001–2009. MMWR, 60, 1337–1342.
  3. 10.1016/j.neubiorev.2018.08.002 · DOI
  4. 10.1523/JNEUROSCI.1798-04.2004 · DOI · Paper page
  5. 10.1089/neu.2014.3534 · DOI · Paper page
  6. 10.1089/neu.2016.4502 · DOI · Paper page
  7. 10.1016/j.ijpsycho.2018.07.474 · DOI · Paper page
  8. 10.1089/neu.2017.5513 · DOI
  9. 10.3109/02699052.2015.1011234 · DOI
  10. 10.1007/s11682-017-9673-3 · DOI
  11. Bigler E.D. (2015). Neuropathology of mild traumatic brain injury: correlation to neurocognitive and neurobehavioral findings., in: Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects., Kobeissy, F.H. (ed). CRC Press/Taylor & Francis: Boca Raton, FL.
  12. 10.1136/bjsports-2016-097393 · DOI
  13. 10.1016/j.jpeds.2014.10.016 · DOI
  14. 10.1097/00001199-199804000-00005 · DOI · Paper page
  15. 10.1017/S1355617708090188 · DOI
  16. 10.1016/j.nicl.2019.102106 · DOI
  17. 10.3109/02699052.2013.793398 · DOI
  18. 10.1097/01241398-199501000-00010 · doi:10.1097/01241398-199501000-00010
  19. 10.1097/BPO.0b013e3181d8fa8c · DOI
  20. 10.1097/BLO.0b013e31805002da · DOI
  21. 10.1080/16501960410023877 · DOI · Paper page
  22. 10.1016/S0140-6736(74)91639-0 · DOI · Paper page
  23. 10.1097/00005373-198501000-00010 · DOI · Paper page
  24. 10.1016/j.ridd.2007.05.002 · DOI
  25. 10.1016/0049-089X(85)90008-0 · DOI · Paper page
  26. Wechsler D. (2011). Wechsler Abbreviated Scale of Intelligence–2nd Edition (WASI-II), manual. Pearson: San Antonio, TX.
  27. 10.1080/09297049.2010.495058 · DOI
  28. 10.1089/neu.2011.1927 · DOI
  29. 10.1002/jnr.24142 · DOI · Paper page
  30. Mayer A.R., , Cohen D.M., , Wertz C.J., , Dodd A.B., , Shoemaker J., , Pluto C., , Zumberge N.A., , Park G., , Bangert B.A., , Lin C., , Minich N.M., , Bacevice A.M., , Bigler E.D., , Campbell R.A., , Hanlon F.M., , Meier T.B., , Oglesbee S.J., , Phillips J.P., , Pottenger A., , Shaff N.A., , Taylor H.G., , Yeo R.A., , Arbogast K.B., , Leddy J.J., , Master C.L., , Mannix R., , Zemek R.L., , and Yeates K.O. (2019). Radiologic common data elements rates in pediatric mild traumatic brain injury. Neurology, 94, e241–e253.
  31. 10.1016/j.neuroimage.2006.01.015 · DOI · Paper page
  32. Avants B.B., , Tustison N., , and Song G. (2008). Advanced normalization tools (ANTS). OR Insight. 1–35.
  33. 10.1016/j.neuroimage.2012.01.021 · DOI · Paper page
  34. Kuznetsova A., , Brockhoff P.B., , and Christensen R.H.B. (2017). lmerTest Package: Tests in linear mixed effects models. J. Stat. Software. 82.
  35. Bates D., , Mächler M., , Bolker B., , and Walker S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Software. 67.
  36. 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
  37. 10.3758/BRM.41.4.1149 · DOI · Paper page
  38. 10.1227/NEU.0000000000000505 · DOI · Paper page
  39. 10.1089/neu.2014.3492 · DOI
  40. Blatter D.D., , Bigler E.D., , Gale S.D., , Johnson S.C., , Anderson C.V., , Burnett B.M., , Ryser D., , Macnamara S.E., , and Bailey B.J. (1997). MR-based brain and cerebrospinal fluid measurement after traumatic brain injury: correlation with neuropsychological outcome. A.J.N.R. Am. J. Neuroradiol. 18, 1–10.
  41. 10.1007/s007010050286 · DOI
  42. 10.1093/cercor/bhv301 · DOI
  43. 10.1080/02699052.2018.1494853 · DOI
  44. 10.1371/journal.pone.0156545 · DOI
  45. 10.1016/j.acn.2005.02.005 · DOI
  46. 10.1371/journal.pone.0038234 · DOI · Paper page
  47. 10.1016/j.neuroimage.2015.10.010 · DOI · Paper page
  48. 10.1016/j.neuroimage.2014.10.005 · DOI
  49. 10.1016/j.dcn.2015.07.008 · DOI
  50. 10.1093/cercor/bhl066 · DOI · Paper page
  51. 10.1016/j.neuron.2011.09.028 · DOI · Paper page
  52. 10.1016/j.ijpsycho.2018.07.474 · DOI · Paper page
  53. Backhausen L.L., , Herting M.M., , Buse J., , Roessner V., , Smolka M.N., , and Vetter N.C. (2016). Quality control of structural MRI images applied using FreeSurfer—a hands-on workflow to rate motion artifacts. Front. Neurosci. 10.
  54. 10.1080/02699052.2018.1494855 · DOI
  55. 10.1016/j.neuroimage.2011.02.029 · DOI
  56. 10.1016/j.neuroimage.2005.09.046 · DOI · Paper page
  57. 10.1016/j.acra.2006.05.001 · DOI
  58. 10.1109/TMI.2006.891486 · DOI · Paper page
  59. 10.1016/j.neuroimage.2014.12.006 · DOI · Paper page
  60. 10.1007/s00247-016-3677-9 · doi:10.1007/s00247-016-3677-9
  61. 10.1016/j.neuroimage.2010.06.017 · DOI
  62. 10.1016/j.biopsych.2005.02.014 · DOI
  63. 10.1006/nimg.2002.1076 · DOI
  64. 10.1016/j.nicl.2017.03.006 · DOI

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Metadata

Authors
Ware, A. L., Goodrich-Hunsaker, N. J., Lebel, C., Shukla, A., Wilde, E. A., Abildskov, T. J., Bigler, E. D., Cohen, D. M., Mihalov, L. K., Bacevice, A., Bangert, B. A., Taylor, H. G., & Yeates, K. O.
Affiliations
  • Department of Psychology, University of Calgary, Calgary, Alberta, Canada.
  • Department of Neurology, University of Utah, Salt Lake City, Utah, USA.
  • Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
  • Abigail Wexner Research Institute at Nationwide Children's Hospital, and Department of Pediatrics, Ohio State University, Columbus, Ohio, USA.
  • Department of Pediatrics, Case Western Reserve University, Cleveland, Ohio, USA.
Venue
Journal of Neurotrauma
Published
2020-08-14
Volume
37
Issue
17
Pages
1892-1901
ISSN
  • 0897-7151
  • 1557-9042
Cited by
24
References
4
Language
en
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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