White Matter Tract Integrity, Involvement in Sports, and Depressive Symptoms in Children

Abstract

White matter tract integrity, measured via fractional anisotropy (FA), may serve as a mediating variable between exercise and depression. To study this, we examined data from 3973 children participating in the ABCD study. Parents of children completed the Sports and Activities questionnaire and the Child Behavior Checklist, and children completed a diffusion MRI scan, providing information about the FA of the parahippocampal cingulum and fornix. Results showed that involvement in sports was associated with reduced depression in boys. The number of activities and sports that a child was involved in was negatively related to FA of the left fornix but was unrelated to FA of other tracts. FA of these white matter tracts was also unrelated to depressive symptoms. This suggests that while white matter tract integrity is associated with exercise, it may not be part of a pathway linking exercise to depression levels in preadolescent boys.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. 1.

    National Institute of Mental Health (2017) Major depression. Available at: https://www.nimh.nih.gov/health/statistics/major-depression.shtml

  2. 2.

    World Health Organization (2004) Part 4: Burden of disease: DALYs. In: Mathers C, Boerma T, Fat DM (eds) The Global Burden of Disease: 2004 Update. WHO Press, Geneva, Switzerland, p 39–52

  3. 3.

    Rao U (2006) Development and natural history of pediatric depression: treatment implications. Clin Neuropsychiatry 3(3):194–204

    Google Scholar 

  4. 4.

    Biddle SJ, Asare M (2011) Physical activity and mental health in children and adolescents: a review of reviews. Br J Sports Med 45(11):886–895

    PubMed Central  Article  PubMed  Google Scholar 

  5. 5.

    Gorham L et al (2019) Involvement in sports, hippocampal volume, and depressive symptoms in children. Biol Psychiatry 4(5):484–492

    Google Scholar 

  6. 6.

    Chaddock L et al (2010) A neuroimaging investigation of the association between aerobic fitness, hippocampal volume, and memory performance in preadolescent children. Brain Res 1358:172–183

    PubMed Central  Article  PubMed  Google Scholar 

  7. 7.

    Herting MM, Keenan MF (2017) Exercise and the developing brain in children and adolescents. Physical activity and the aging brain. Academic Press, Cambridge, pp 13–19

    Google Scholar 

  8. 8.

    Stephens T (1988) Physical activity and mental health in the United States and Canada: evidence from four population surveys. Prev Med 17(1):35–47

    Article  PubMed  Google Scholar 

  9. 9.

    Singh NA, Clements KM, Singh MA (2001) The efficacy of exercise as a long-term antidepressant in elderly subjects: a randomized, controlled trial. J Gerontol Ser A 56(8):M497–M504

    Article  Google Scholar 

  10. 10.

    van Praag H et al (1999) Running enhances neurogenesis, learning, and long-term potentiation in mice. Proc Natl Acad Sci USA 96(23):13427–13431

    Article  PubMed  Google Scholar 

  11. 11.

    Yuede CM et al (2009) Effects of voluntary and forced exercise on plaque deposition, hippocampal volume, and behavior in the Tg2576 mouse model of Alzheimer's disease. Neurobiol Dis 35(3):426–432

    PubMed Central  Article  PubMed  Google Scholar 

  12. 12.

    Schmaal L et al (2016) Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Mol Psychiatry 21(6):806–812

    Article  PubMed  Google Scholar 

  13. 13.

    Cheng YQ et al (2010) Brain volume alteration and the correlations with the clinical characteristics in drug-naive first-episode MDD patients: a voxel-based morphometry study. Neurosci Lett 480(1):30–34

    Article  PubMed  Google Scholar 

  14. 14.

    MacMaster FP, Kusumakar V (2004) Hippocampal volume in early onset depression. BMC Med 2:2

    PubMed Central  Article  PubMed  Google Scholar 

  15. 15.

    Malykhin NV et al (2010) Structural changes in the hippocampus in major depressive disorder: contributions of disease and treatment. J Psychiatry Neurosci 35(5):337–343

    PubMed Central  Article  PubMed  Google Scholar 

  16. 16.

    McKinnon MC et al (2009) A meta-analysis examining clinical predictors of hippocampal volume in patients with major depressive disorder. J Psychiatry Neurosci 34(1):41–54

    PubMed Central  PubMed  Google Scholar 

  17. 17.

    Videbech P, Ravnkilde B (2004) Hippocampal volume and depression: a meta-analysis of MRI studies. Am J Psychiatry 161(11):1957–1966

    Article  PubMed  Google Scholar 

  18. 18.

    Arnone D et al (2013) State-dependent changes in hippocampal grey matter in depression. Mol Psychiatry 18(12):1265–1272

    Article  PubMed  Google Scholar 

  19. 19.

    Cotman CW, Berchtold NC (2002) Exercise: a behavioral intervention to enhance brain health and plasticity. Trends Neurosci 25(6):295–301

    Article  PubMed  Google Scholar 

  20. 20.

    Lopez-Lopez C, LeRoith D, Torres-Aleman I (2004) Insulin-like growth factor I is required for vessel remodeling in the adult brain. Proc Natl Acad Sci USA 101(26):9833–9838

    Article  PubMed  Google Scholar 

  21. 21.

    Mueller BA et al (2015) Diffusion MRI and its role in neuropsychology. Neuropsychol Rev 25(3):250–271

    PubMed Central  Article  PubMed  Google Scholar 

  22. 22.

    Kieseppa T et al (2010) Major depressive disorder and white matter abnormalities: a diffusion tensor imaging study with tract-based spatial statistics. J Affect Disord 120(1–3):240–244

    Article  PubMed  Google Scholar 

  23. 23.

    Zhu X et al (2011) Altered white matter integrity in first-episode, treatment-naive young adults with major depressive disorder: a tract-based spatial statistics study. Brain Res 1369:223–229

    Article  PubMed  Google Scholar 

  24. 24.

    de Diego-Adelino J et al (2014) Microstructural white-matter abnormalities associated with treatment resistance, severity and duration of illness in major depression. Psychol Med 44(6):1171–1182

    Article  PubMed  Google Scholar 

  25. 25.

    Huang H et al (2011) White matter changes in healthy adolescents at familial risk for unipolar depression: a diffusion tensor imaging study. Neuropsychopharmacology 36(3):684–691

    Article  PubMed  Google Scholar 

  26. 26.

    Keedwell PA et al (2012) Cingulum white matter in young women at risk of depression: the effect of family history and anhedonia. Biol Psychiatry 72(4):296–302

    Article  PubMed  Google Scholar 

  27. 27.

    Marks BL et al (2007) Role of aerobic fitness and aging on cerebral white matter integrity. Ann N Y Acad Sci 1097:171–174

    Article  PubMed  Google Scholar 

  28. 28.

    Sexton CE et al (2016) A systematic review of MRI studies examining the relationship between physical fitness and activity and the white matter of the ageing brain. Neuroimage 131:81–90

    PubMed Central  Article  PubMed  Google Scholar 

  29. 29.

    Schmithorst VJ, Holland SK, Dardzinski BJ (2008) Developmental differences in white matter architecture between boys and girls. Hum Brain Mapp 29(6):696–710

    PubMed Central  Article  PubMed  Google Scholar 

  30. 30.

    Iacono WG et al (2018) The utility of twins in developmental cognitive neuroscience research: how twins strengthen the ABCD research design. Dev Cogn Neurosci 32:30–42

    Article  PubMed  Google Scholar 

  31. 31.

    Garavan H et al (2018) Recruiting the ABCD sample: design considerations and procedures. Dev Cogn Neurosci 32:16–22

    PubMed Central  Article  PubMed  Google Scholar 

  32. 32.

    Barch DM et al (2018) Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description. Dev Cogn Neurosci 32:55–66

    Article  PubMed  Google Scholar 

  33. 33.

    Achenbach TM, Rescorla LA (2001) Manual for the ASEBA school-age forms and profiles. In: Vermont UO (ed) Research Center for Children, Youth and Families. Burlington, VT, University of Vermont

    Google Scholar 

  34. 34.

    Hagler DJ et al. (2018) Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. bioRxiv

  35. 35.

    Tisdall MD et al (2012) Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn Reson Med 68(2):389–399

    Article  PubMed  Google Scholar 

  36. 36.

    White N et al (2010) PROMO: real-time prospective motion correction in MRI using image-based tracking. Magn Reson Med 63(1):91–105

    PubMed Central  Article  PubMed  Google Scholar 

  37. 37.

    Moeller S et al (2010) Multiband multislice GE-EPI at 7 T, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Reson Med 63(5):1144–1153

    PubMed Central  Article  PubMed  Google Scholar 

  38. 38.

    Setsompop K et al (2012) Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med 67(5):1210–1224

    Article  PubMed  Google Scholar 

  39. 39.

    Alexander AL et al (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4(3):316–329

    PubMed Central  Article  PubMed  Google Scholar 

  40. 40.

    Hagler DJ et al (2009) Automated white-matter tractography using a probabilistic diffusion tensor atlas: application to temporal lobe epilepsy. Hum Brain Mapp 30(5):1535–1547

    PubMed Central  Article  PubMed  Google Scholar 

  41. 41.

    Benjamini Y, Hochberg H (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57:289–300

    Google Scholar 

  42. 42.

    Benjamini Y, Yekutieli D (2001) The control of the false discovery rate: a practical and powerful approach to multiple testing. Ann Stat 29(4):1165–1188

    Article  Google Scholar 

  43. 43.

    Smith JC et al (2016) Interactive effects of physical activity and APOE-epsilon4 on white matter tract diffusivity in healthy elders. Neuroimage 131:102–112

    Article  PubMed  Google Scholar 

  44. 44.

    Svatkova A et al (2015) Physical exercise keeps the brain connected: biking increases white matter integrity in patients with schizophrenia and healthy controls. Schizophr Bull 41(4):869–878

    PubMed Central  Article  PubMed  Google Scholar 

  45. 45.

    Rogers CE et al (2016) Regional white matter development in very preterm infants: perinatal predictors and early developmental outcomes. Pediatr Res 79(1–1):87–95

    Article  PubMed  Google Scholar 

  46. 46.

    Ben Bashat D et al (2007) Accelerated maturation of white matter in young children with autism: a high b value DWI study. Neuroimage 37(1):40–47

    Article  PubMed  Google Scholar 

  47. 47.

    Billeci L et al (2012) White matter connectivity in children with autism spectrum disorders: a tract-based spatial statistics study. BMC Neurol 12:148

    PubMed Central  Article  PubMed  Google Scholar 

  48. 48.

    Wolff JJ et al (2012) Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry 169(6):589–600

    PubMed Central  Article  PubMed  Google Scholar 

Download references

Funding

Funding was provided by National Institutes of Health (Grant Number U01A005020803).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lisa S. Gorham.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gorham, L.S., Barch, D.M. White Matter Tract Integrity, Involvement in Sports, and Depressive Symptoms in Children. Child Psychiatry Hum Dev 51, 490–501 (2020). https://doi.org/10.1007/s10578-020-00960-3

Download citation

Keywords

  • Diffusion MRI
  • Exercise
  • Depression
  • Children
  • White matter tract integrity