Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 41

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Imaging in Pediatric Concussion: A Systematic Review

Schmidt, Julia, Hayward, Kathryn S., Brown, Katlyn E., Zwicker, Jill G., Ponsford, Jennie, van Donkelaar, Paul, Babul, Shelina, Boyd, Lara A.·2018·Pediatrics
DOI 10.1542/peds.2017-3406
Open this paper in CoChat — free account

Abstract

CONTEXT: Pediatric mild traumatic brain injury (mTBI) is a common and poorly understood injury. Neuroimaging indexes brain injury and outcome after pediatric mTBI, but remains largely unexplored. OBJECTIVE: To investigate the differences in neuroimaging findings in children/youth with mTBI. Measures of behavior, symptoms, time since injury, and age at injury were also considered. DATA SOURCES: A systematic review was conducted up to July 6, 2016. STUDY SELECTION: Studies were independently screened by 2 authors and included if they met predetermined eligibility criteria: (1) children/youth (5–18 years of age), (2) diagnosis of mTBI, and (3) use of neuroimaging. DATA EXTRACTION: Two authors independently appraised study quality and extracted demographic and outcome data. RESULTS: Twenty-two studies met the eligibility criteria, involving 448 participants with mTBI (mean age = 12.7 years ± 2.8). Time postinjury ranged from 1 day to 5 years. Seven different neuroimaging methods were investigated in included studies. The most frequently used method, diffusion tensor imaging (41%), had heterogeneous findings with respect to the specific regions and tracts that showed group differences. However, group differences were observed in many regions containing the corticospinal tract, portions of the corpus callosum, or frontal white-matter regions; fractional anisotropy was increased in 88% of the studies. LIMITATIONS: This review included a heterogeneous sample with regard to participant ages, time since injury, symptoms, and imaging methods which prevented statistical pooling/modelling. CONCLUSIONS: These data highlight essential priorities for future research (eg, common data elements) that are foundational to progress the understanding of pediatric concussion.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1542/peds.2017-3406

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 8 references

Research works printed but not linked: 72

  1. Willer, 2004, A population based investigation of head injuries and symptoms of concussion of children and adolescents in schools., Inj Prev, 10, 144, 10.1136/ip.2003.005017 · DOI
  2. Willer, 2006, Management of concussion and post-concussion syndrome., Curr Treat Options Neurol, 8, 415, 10.1007/s11940-006-0031-9 · DOI
  3. Anderson, 2001, Outcome from mild head injury in young children: a prospective study., J Clin Exp Neuropsychol, 23, 705, 10.1076/jcen.23.6.705.1015 · DOI
  4. Davis, 2017, What is the difference in concussion management in children as compared with adults? A systematic review., Br J Sports Med, 51, 949, 10.1136/bjsports-2016-097415 · DOI · Paper page
  5. McCrea, 2008, Mild Traumatic Brain Injury and Postconcussion Syndrome: The New Evidence Base for Diagnosis and Treatment
  6. Bigler, 2013, Neuroimaging and social behavior in children after traumatic brain injury: findings from the Social Outcomes of Brain Injury in Kids (SOBIK) study., NeuroRehabilitation, 32, 707, 10.3233/NRE-130896 · DOI
  7. Dziemianowicz, 2012, Sports-related concussion testing., Curr Neurol Neurosci Rep, 12, 547, 10.1007/s11910-012-0299-y · DOI
  8. Wright, 2016, Myelin water fraction is transiently reduced after a single mild traumatic brain injury—a prospective cohort study in collegiate hockey players., PLoS One, 11, e0150215, 10.1371/journal.pone.0150215 · DOI
  9. Shenton, 2012, A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury., Brain Imaging Behav, 6, 137, 10.1007/s11682-012-9156-5 · DOI · Paper page
  10. Bernhardt, 2017, Agreed definitions and a shared vision for new standards in stroke recovery research: the Stroke Recovery and Rehabilitation Roundtable taskforce., Int J Stroke, 12, 444, 10.1177/1747493017711816 · DOI · Paper page
  11. Kamins, 2017, What is the physiological time to recovery after concussion? A systematic review., Br J Sports Med, 51, 935, 10.1136/bjsports-2016-097464 · DOI · Paper page
  12. Arfanakis, 2002, Diffusion tensor MR imaging in diffuse axonal injury., AJNR Am J Neuroradiol, 23, 794
  13. McCrea, 2017, Role of advanced neuroimaging, fluid biomarkers and genetic testing in the assessment of sport-related concussion: a systematic review., Br J Sports Med, 51, 919, 10.1136/bjsports-2016-097447 · DOI · Paper page
  14. Major, 2015, Using transcranial magnetic stimulation to quantify electrophysiological changes following concussive brain injury: a systematic review., Clin Exp Pharmacol Physiol, 42, 394, 10.1111/1440-1681.12363 · DOI
  15. Lefebvre, 2015, Probing the effects of mild traumatic brain injury with transcranial magnetic stimulation of the primary motor cortex., Brain Inj, 29, 1032, 10.3109/02699052.2015.1028447 · DOI
  16. Borich, 2013, Combining whole-brain voxel-wise analysis with in vivo tractography of diffusion behavior after sports-related concussion in adolescents: a preliminary report., J Neurotrauma, 30, 1243, 10.1089/neu.2012.2818 · DOI
  17. Mayer, 2012, Diffusion abnormalities in pediatric mild traumatic brain injury., J Neurosci, 32, 17961, 10.1523/JNEUROSCI.3379-12.2012 · DOI · Paper page
  18. Virji-Babul, 2013, Diffusion tensor imaging of sports-related concussion in adolescents., Pediatr Neurol, 48, 24, 10.1016/j.pediatrneurol.2012.09.005 · DOI
  19. Keightley, 2014, A functional magnetic resonance imaging study of working memory in youth after sports-related concussion: is it still working?, J Neurotrauma, 31, 437, 10.1089/neu.2013.3052 · DOI
  20. Krivitzky, 2011, Functional magnetic resonance imaging of working memory and response inhibition in children with mild traumatic brain injury., J Int Neuropsychol Soc, 17, 1143, 10.1017/S1355617711001226 · DOI · Paper page
  21. Saluja, 2015, Navigational memory functional magnetic resonance imaging: a test for concussion in children., J Neurotrauma, 32, 712, 10.1089/neu.2014.3470 · DOI
  22. Yeates, 2017, Advancing Concussion Assessment in Pediatrics (A-CAP): a prospective, concurrent cohort, longitudinal study of mild traumatic brain injury in children: protocol study., BMJ Open, 7, e017012, 10.1136/bmjopen-2017-017012 · DOI
  23. Treadwell, 2012, A framework for best evidence approaches can improve the transparency of systematic reviews., J Clin Epidemiol, 65, 1159, 10.1016/j.jclinepi.2012.06.001 · DOI
  24. Slavin, 1995, Best evidence synthesis: an intelligent alternative to meta-analysis., J Clin Epidemiol, 48, 9, 10.1016/0895-4356(94)00097-A · DOI · Paper page
  25. Letzel, 1995, “Best-evidence synthesis: an intelligent alternative to meta-analysis”: discussion. A case of “either-or” or “as well”., J Clin Epidemiol, 48, 19, 10.1016/0895-4356(94)00135-D · DOI
  26. Alexander, 2007, Diffusion tensor imaging of the brain., Neurotherapeutics, 4, 316, 10.1016/j.nurt.2007.05.011 · DOI · Paper page
  27. Barzó, 1997, Contribution of vasogenic and cellular edema to traumatic brain swelling measured by diffusion-weighted imaging., J Neurosurg, 87, 900, 10.3171/jns.1997.87.6.0900 · DOI · Paper page
  28. Rosenblum, 2007, Cytotoxic edema: monitoring its magnitude and contribution to brain swelling., J Neuropathol Exp Neurol, 66, 771, 10.1097/nen.0b013e3181461965 · DOI
  29. Levine, 2008, The Toronto traumatic brain injury study: injury severity and quantified MRI., Neurology, 70, 771, 10.1212/01.wnl.0000304108.32283.aa · DOI
  30. Dettwiler, 2014, Persistent differences in patterns of brain activation after sports-related concussion: a longitudinal functional magnetic resonance imaging study., J Neurotrauma, 31, 180, 10.1089/neu.2013.2983 · DOI
  31. Hillary, 2008, Neuroimaging of working memory dysfunction and the dilemma with brain reorganization hypotheses, Journal of the International Neuropsychological So, 14, 526, 10.1017/S1355617708080788 · DOI · Paper page
  32. Mayer, 2015, Functional magnetic resonance imaging of mild traumatic brain injury., Neurosci Biobehav Rev, 49, 8, 10.1016/j.neubiorev.2014.11.016 · DOI
  33. Raichle, 2001, A default mode of brain function., Proc Natl Acad Sci USA, 98, 676, 10.1073/pnas.98.2.676 · DOI · Paper page
  34. Zhou, 2012, Default-mode network disruption in mild traumatic brain injury., Radiology, 265, 882, 10.1148/radiol.12120748 · DOI · Paper page
  35. Haacke, 2004, Susceptibility weighted imaging (SWI)., Magn Reson Med, 52, 612, 10.1002/mrm.20198 · DOI · Paper page
  36. Fiser, 2000, Relationship of pediatric overall performance category and pediatric cerebral performance category scores at pediatric intensive care unit discharge with outcome measures collected at hospital discharge and 1- and 6-month follow-up assessments., Crit Care Med, 28, 2616, 10.1097/00003246-200007000-00072 · DOI · Paper page
  37. Tong, 2003, Hemorrhagic shearing lesions in children and adolescents with posttraumatic diffuse axonal injury: improved detection and initial results., Radiology, 227, 332, 10.1148/radiol.2272020176 · DOI · Paper page
  38. Agam, 2007, Interactions between working memory and visual perception: an ERP/EEG study., Neuroimage, 36, 933, 10.1016/j.neuroimage.2007.04.014 · DOI
  39. Moore, 2015, The persistent influence of pediatric concussion on attention and cognitive control during flanker performance., Biol Psychol, 109, 93, 10.1016/j.biopsycho.2015.04.008 · DOI
  40. Vagnozzi, 2010, Assessment of metabolic brain damage and recovery following mild traumatic brain injury: a multicentre, proton magnetic resonance spectroscopic study in concussed patients., Brain, 133, 3232, 10.1093/brain/awq200 · DOI · Paper page
  41. Babcock, 2015, White matter alterations in youth with acute mild traumatic brain injury., J Pediatr Rehabil Med, 8, 285, 10.3233/PRM-150347 · DOI
  42. Baillargeon, 2012, Neuropsychological and neurophysiological assessment of sport concussion in children, adolescents and adults., Brain Inj, 26, 211, 10.3109/02699052.2012.654590 · DOI
  43. Beauchamp, 2013, Susceptibility weighted imaging and its relationship to outcome after pediatric traumatic brain injury., Cortex, 49, 591, 10.1016/j.cortex.2012.08.015 · DOI
  44. Bigler, 2013, Heterogeneity of brain lesions in pediatric traumatic brain injury., Neuropsychology, 27, 438, 10.1037/a0032837 · DOI
  45. Borich, 2015, Alterations in resting-state brain networks in concussed adolescent athletes., J Neurotrauma, 32, 265, 10.1089/neu.2013.3269 · DOI
  46. Chu, 2010, Voxel-based analysis of diffusion tensor imaging in mild traumatic brain injury in adolescents., AJNR Am J Neuroradiol, 31, 340, 10.3174/ajnr.A1806 · DOI · Paper page
  47. Levin, 2002, Working memory after traumatic brain injury in children., Ann Neurol, 52, 82, 10.1002/ana.10252 · DOI
  48. Maugans, 2012, Pediatric sports-related concussion produces cerebral blood flow alterations., Pediatrics, 129, 28, 10.1542/peds.2011-2083 · DOI · Paper page
  49. Sinopoli, 2014, Imaging “brain strain” in youth athletes with mild traumatic brain injury during dual-task performance., J Neurotrauma, 31, 1843, 10.1089/neu.2014.3326 · DOI
  50. Van Beek, 2015, Mathematical difficulties and white matter abnormalities in subacute pediatric mild traumatic brain injury., J Neurotrauma, 32, 1567, 10.1089/neu.2014.3809 · DOI
  51. Van Beek, 2015, Longitudinal changes in mathematical abilities and white matter following paediatric mild traumatic brain injury., Brain Inj, 29, 1701, 10.3109/02699052.2015.1075172 · DOI
  52. Virji-Babul, 2014, Changes in functional brain networks following sports-related concussion in adolescents., J Neurotrauma, 31, 1914, 10.1089/neu.2014.3450 · DOI
  53. Westfall, 2015, Increased brain activation during working memory processing after pediatric mild traumatic brain injury (mTBI)., J Pediatr Rehabil Med, 8, 297, 10.3233/PRM-150348 · DOI · Paper page
  54. Yang, 2012, An FMRI study of auditory orienting and inhibition of return in pediatric mild traumatic brain injury., J Neurotrauma, 29, 2124, 10.1089/neu.2012.2395 · DOI · Paper page
  55. Teasdale, 1974, Assessment of coma and impaired consciousness. A practical scale., Lancet, 2, 81, 10.1016/S0140-6736(74)91639-0 · DOI · Paper page
  56. Marmarou, 1994, Traumatic brain edema: an overview., Acta Neurochir Suppl (Wien), 60, 421
  57. Inglese, 2005, Diffuse axonal injury in mild traumatic brain injury: a diffusion tensor imaging study., J Neurosurg, 103, 298, 10.3171/jns.2005.103.2.0298 · DOI · Paper page
  58. Miles, 2008, Short-term DTI predictors of cognitive dysfunction in mild traumatic brain injury., Brain Inj, 22, 115, 10.1080/02699050801888816 · DOI
  59. Lipton, 2008, Multifocal white matter ultrastructural abnormalities in mild traumatic brain injury with cognitive disability: a voxel-wise analysis of diffusion tensor imaging., J Neurotrauma, 25, 1335, 10.1089/neu.2008.0547 · DOI
  60. Ling, 2012, Biomarkers of increased diffusion anisotropy in semi-acute mild traumatic brain injury: a longitudinal perspective., Brain, 135, 1281, 10.1093/brain/aws073 · DOI
  61. Wilde, 2012, Serial measurement of memory and diffusion tensor imaging changes within the first week following uncomplicated mild traumatic brain injury., Brain Imaging Behav, 6, 319, 10.1007/s11682-012-9174-3 · DOI
  62. Sarnat, 2003, Functions of the corticospinal and corticobulbar tracts in the human newborn., J Pediatr Neurol, 1, 3
  63. Nezu, 1997, Magnetic stimulation of motor cortex in children: maturity of corticospinal pathway and problem of clinical application., Brain Dev, 19, 176, 10.1016/S0387-7604(96)00552-9 · DOI
  64. Jones, 2009, Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials [published correction appears in Clin Trials. 2009;6(3):288]., Clin Trials, 6, 16, 10.1177/1740774508100984 · DOI
  65. McCauley, 2012, Recommendations for the use of common outcome measures in pediatric traumatic brain injury research., J Neurotrauma, 29, 678, 10.1089/neu.2011.1838 · DOI · Paper page
  66. Giedd, 1999, Brain development during childhood and adolescence: a longitudinal MRI study., Nat Neurosci, 2, 861, 10.1038/13158 · DOI · Paper page
  67. Brown, 2005, Developmental changes in human cerebral functional organization for word generation., Cereb Cortex, 15, 275, 10.1093/cercor/bhh129 · DOI · Paper page
  68. Greer, 2011, Diffuse traumatic axonal injury in the mouse induces atrophy, c-Jun activation, and axonal outgrowth in the axotomized neuronal population., J Neurosci, 31, 5089, 10.1523/JNEUROSCI.5103-10.2011 · DOI
  69. Kirkwood, 2008, Management of pediatric mild traumatic brain injury: a neuropsychological review from injury through recovery., Clin Neuropsychol, 22, 769, 10.1080/13854040701543700 · DOI · Paper page
  70. Shultz, 2017, The potential for animal models to provide insight into mild traumatic brain injury: translational challenges and strategies., Neurosci Biobehav Rev, 76, 396, 10.1016/j.neubiorev.2016.09.014 · DOI
  71. Plenger, 2016, fNIRS-based investigation of the Stroop task after TBI., Brain Imaging Behav, 10, 357, 10.1007/s11682-015-9401-9 · DOI
  72. Rajapakse, 2013, Non-invasive brain stimulation in children: applications and future directions., Transl Neurosci, 4, 10.2478/s13380-013-0116-3 · DOI

Cited by

See all 41

Metadata

Authors
Schmidt, J., Hayward, K. S., Brown, K. E., Zwicker, J. G., Ponsford, J., van Donkelaar, P., Babul, S., & Boyd, L. A.
Affiliations
  • Department of Physical Therapy, The University of British Columbia, Vancouver, Canada;
  • Monash University, Clayton, Australia
Venue
Pediatrics
Publisher
American Academy of Pediatrics (AAP)
Published
2018-05-01
Volume
141
Issue
5
ISSN
  • 0031-4005
  • 1098-4275
DOI
10.1542/peds.2017-3406
Cited by
41
References
8
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Imaging in Pediatric Concussion: A Systematic Review. https://app.cochat.ai/papers/10.1542/peds.2017-3406
The paper
Schmidt, J., Hayward, K. S., Brown, K. E., Zwicker, J. G., Ponsford, J., van Donkelaar, P., Babul, S., & Boyd, L. A. (2018). Imaging in Pediatric Concussion: A Systematic Review. Pediatrics. https://doi.org/10.1542/peds.2017-3406

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.