Advertisement

Neuroradiology

, Volume 60, Issue 12, pp 1343–1351 | Cite as

Assessment of myelination progression in subcortical white matter of children aged 6–48 months using T2-weighted imaging

  • Congcong Liu
  • Chao Jin
  • Zhijie Jian
  • Miaomiao Wang
  • Xianjun Li
  • Heng Liu
  • Qinli Sun
  • Lingxia Zeng
  • Jian Yang
Paediatric Neuroradiology

Abstract

Purpose

This study aims to provide a screening scoring method by assessing the age-related change of subcortical white matter (WM) myelination via T2-weighted imaging (T2WI).

Methods

This study retrospectively recruited 109 children aged 6–48 months without abnormalities on MRI. Based on Parazzini’s study, we developed a modified T2WI-based method to assess subcortical WM myelination (frontal, temporal, parietal, occipital lobes, and insula) by scoring WM’s signal changes. Inter- and intra-observer agreements were evaluated by Bland-Altman plot. Age-related changes of myelination score were explored by locally weighted scatterplot smoothing (LOESS), linear regression, and Spearman correlation coefficients (r). Relationships between diffusion tensor imaging (DTI) metrics and total myelination score were investigated to further validate practicability of the scoring method by tract-based spatial statistics (TBSS).

Results

This method showed good intra-observer (mean difference = 0.18, SD = 0.95) and inter-observer agreements (mean difference = − 0.06, SD = 1.01). The LOESS and linear regression results indicated that myelination proceeded in two phases: a period of rapid growth (6–20 months; slope = 0.561) and one of slower growth (21–48 months; slope = 0.097). Significant correlations between myelination score and age were observed in whole subcortical WM (r = 0.945; P < 0.001) and all regional subcortical WM (r_mean = 0.819, range, 0.664–0.928; P < 0.001). TBSS found significant correlations of WM-DTI metrics with myelination score during the range of 6–20 months, while no significant correlation was observed in 21–48 months.

Conclusion

The modified T2WI-based screening scoring method is easily feasible to assess myelination progression of subcortical WM, especially suitable for children aged 6–20 months. It may show potential in identifying individual developmental abnormalities by scoring assessment in the future clinical practice.

Keywords

Children Subcortical white matter Myelination T2WI DTI 

Abbreviations

AD

Axial diffusivity

DTI

Diffusion tensor imaging

FA

Fractional anisotropy

FMRIB

Functional MRI of the brain

FSL

FMRIB software library

FSE

Fast spin echo

FLAIR

Fluid-attenuated inversion recovery

FOV

Field of view

LOESS

Locally weighted scatterplot smoothing model

RD

Radial diffusivity

SWI

Susceptibility weighted imaging

TBSS

Tract-based spatial statistics

T2WI

T2-weighted imaging

T1WI

T1-weighted imaging

WM

White matter

Notes

Compliance with ethical standards

Funding

This study was funded by the National Key Research and Development Program of China (2016YFC0100300), the National Natural Science Foundation of China (Nos. 81171317, 81471631, 81771810 and 51706178), the 2011 New Century Excellent Talent Support Plan of the Ministry of Education, China (NCET-11-0438), the China Postdoctoral Science Foundation (No. 2017M613145), the Shaanxi Provincial Natural Science Foundation for Youths of China (No. 2017JQ8005) and the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University (No. XJTU1AF-CRF-2015-004).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

234_2018_2108_Fig6_ESM.png (147 kb)
Supplementary Fig. 1

Spearman correlation coefficient of the myelination score of peritrigonal region and age. (PNG 147 kb)

234_2018_2108_MOESM1_ESM.tif (17.2 mb)
High resolution image (TIF 17618 kb)
234_2018_2108_Fig7_ESM.png (1.7 mb)
Supplementary Fig. 2

Spearman correlations of white matter DTI metrics with age of 6–20 months. Green color indicates the fibrous skeleton of brain white matter and represents no significant correlation. The red-yellow color scale indicates the positively correlated regions and blue- light blue color scale indicates the negatively correlated regions. (PNG 1766 kb)

234_2018_2108_MOESM2_ESM.tif (19.1 mb)
High resolution image (TIF 19554 kb)
234_2018_2108_Fig8_ESM.png (1.1 mb)
Supplementary Fig. 3

Spearman correlations of white matter DTI metrics with age of 21–48 months. Green color indicates the fibrous skeleton of brain white matter and represents no significant correlation. The blue- light blue color scale indicates the negatively correlated regions. (PNG 1142 kb)

234_2018_2108_MOESM3_ESM.tif (20.6 mb)
High resolution image (TIF 21073 kb)
234_2018_2108_MOESM4_ESM.docx (38 kb)
ESM 1 (DOCX 38 kb)

References

  1. 1.
    Barkovich AJ (2005) Magnetic resonance techniques in the assessment of myelin and myelination. J Inherit Metab Dis 28(3):311–343.  https://doi.org/10.1007/s10545-005-5952-z CrossRefPubMedGoogle Scholar
  2. 2.
    Barkovich AJ, Kjos BO, Jackson DE Jr, Norman D (1988) Normal maturation of the neonatal and infant brain: MR imaging at 1.5 T. Radiology 166(1 Pt 1):173–180.  https://doi.org/10.1148/radiology.166.1.3336675 CrossRefPubMedGoogle Scholar
  3. 3.
    Barkovich AJ (2000) Concepts of myelin and myelination in neuroradiology. AJNR Am J Neuroradiol 21(6):1099–1109PubMedGoogle Scholar
  4. 4.
    Huppi PS, Dubois J (2006) Diffusion tensor imaging of brain development. Semin Fetal Neonatal Med 11(6):489–497.  https://doi.org/10.1016/j.siny.2006.07.006 CrossRefPubMedGoogle Scholar
  5. 5.
    Callaghan MF, Freund P, Draganski B, Anderson E, Cappelletti M, Chowdhury R, Diedrichsen J, Fitzgerald TH, Smittenaar P, Helms G, Lutti A, Weiskopf N (2014) Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiol Aging 35(8):1862–1872.  https://doi.org/10.1016/j.neurobiolaging.2014.02.008 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Song S-K, Sun S-W, Ramsbottom MJ, Chang C, Russell J, Cross AH (2002) Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage 17(3):1429–1436.  https://doi.org/10.1006/nimg.2002.1267 CrossRefPubMedGoogle Scholar
  7. 7.
    Gao W, Lin W, Chen Y, Gerig G, Smith JK, Jewells V, Gilmore JH (2009) Temporal and spatial development of axonal maturation and myelination of white matter in the developing brain. AJNR Am J Neuroradiol 30(2):290–296.  https://doi.org/10.3174/ajnr.A1363 CrossRefPubMedGoogle Scholar
  8. 8.
    Parazzini C, Baldoli C, Scotti G, Triulzi F (2002) Terminal zones of myelination: MR evaluation of children aged 20-40 months. AJNR Am J Neuroradiol 23(10):1669–1673PubMedGoogle Scholar
  9. 9.
    N B (1993) Bayley scales of infant development, 2nd edn. Psychological Corporation, San AntonioGoogle Scholar
  10. 10.
    Augustyniak M, Mrozek-Budzyn D, Kieltyka A, Majewska R (2013) Stability of the mental and motor Bayley Scales of Infant Development (2nd ed.) in infants over first three years of life. Przegl Epidemiol 67 (3):483–486, 581-484Google Scholar
  11. 11.
    Vade A, Sukhani R, Dolenga M, Habisohn-Schuck C (1995) Chloral hydrate sedation of children undergoing CT and MR imaging: safety as judged by American Academy of Pediatrics guidelines. AJR Am J Roentgenol 165(4):905–909.  https://doi.org/10.2214/ajr.165.4.7676990 CrossRefPubMedGoogle Scholar
  12. 12.
    Bracken J, Heaslip I, Ryan S (2012) Chloral hydrate sedation in radiology: retrospective audit of reduced dose. Pediatr Radiol 42(3):349–354.  https://doi.org/10.1007/s00247-011-2279-9 CrossRefPubMedGoogle Scholar
  13. 13.
    Zarifi MK, Evlogias N, Vritsiou M, Theodoropoulos V (1995) Medium dose chloral hydrate for sedation of infants and children undergoing CT. Eur Radiol 5:524–527CrossRefGoogle Scholar
  14. 14.
    Kauffman RBW, Berlin C et al (1993) American Academy of Pediatrics Committee on Drugs and Committee on Environmental Health: use of chloral hydrate for sedation in children. Pediatrics 92(3):471–473Google Scholar
  15. 15.
    Ball G, Counsell SJ, Anjari M, Merchant N, Arichi T, Doria V, Rutherford MA, Edwards AD, Rueckert D, Boardman JP (2010) An optimised tract-based spatial statistics protocol for neonates: applications to prematurity and chronic lung disease. NeuroImage 53(1):94–102.  https://doi.org/10.1016/j.neuroimage.2010.05.055 CrossRefPubMedGoogle Scholar
  16. 16.
    Li X, Gao J, Wang M, Wan M, Yang J (2016) Rapid and reliable tract-based spatial statistics pipeline for diffusion tensor imaging in the neonatal brain: applications to the white matter development and lesions. Magn Reson Imaging 34(9):1314–1321.  https://doi.org/10.1016/j.mri.2016.07.011 CrossRefPubMedGoogle Scholar
  17. 17.
    Li X, Gao J, Wang M, Zheng J, Li Y, Hui ES, Wan M, Yang J (2017) Characterization of extensive microstructural variations associated with punctate white matter lesions in preterm neonates. AJNR Am J Neuroradiol 38(6):1228–1234.  https://doi.org/10.3174/ajnr.A5226 CrossRefPubMedGoogle Scholar
  18. 18.
    Welker KM, Patton A (2012) Assessment of normal myelination with magnetic resonance imaging. Semin Neurol 32(1):15–28.  https://doi.org/10.1055/s-0032-1306382 CrossRefPubMedGoogle Scholar
  19. 19.
    Dietrich RB, Bradley WG, Zaragoza EJ, Otto RJ, Taira RK, Wilson GH, Kangarloo H (1988) MR evaluation of early myelination patterns in normal and developmentally delayed infants. AJR Am J Roentgenol 150(4):889–896.  https://doi.org/10.2214/ajr.150.4.889 CrossRefPubMedGoogle Scholar
  20. 20.
    Maricich SM, Azizi P, Jones JY, Morriss MC, Hunter JV, Smith EO, Miller G (2007) Myelination as assessed by conventional MR imaging is normal in young children with idiopathic developmental delay. AJNR Am J Neuroradiol 28(8):1602–1605.  https://doi.org/10.3174/ajnr.A0602 CrossRefPubMedGoogle Scholar
  21. 21.
    Dubois J, Dehaene-Lambertz G, Kulikova S, Poupon C, Huppi PS, Hertz-Pannier L (2014) The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience 276:48–71.  https://doi.org/10.1016/j.neuroscience.2013.12.044 CrossRefPubMedGoogle Scholar
  22. 22.
    Dennis EL, Jahanshad N, McMahon KL, de Zubicaray GI, Martin NG, Hickie IB, Toga AW, Wright MJ, Thompson PM (2014) Development of insula connectivity between ages 12 and 30 revealed by high angular resolution diffusion imaging. Hum Brain Mapp 35(4):1790–1800.  https://doi.org/10.1002/hbm.22292 CrossRefPubMedGoogle Scholar
  23. 23.
    Barkovich AJMP (2011) Normal development of the neonatal and infant brain, skull, and spine. Pediatric Neuroimaging:20–80Google Scholar
  24. 24.
    Geng X, Gouttard S, Sharma A, Gu H, Styner M, Lin W, Gerig G, Gilmore JH (2012) Quantitative tract-based white matter development from birth to age 2years. NeuroImage 61(3):542–557.  https://doi.org/10.1016/j.neuroimage.2012.03.057 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Hermoye L, Saint-Martin C, Cosnard G, Lee S-K, Kim J, Nassogne M-C, Menten R, Clapuyt P, Donohue PK, Hua K, Wakana S, Jiang H, van Zijl PCM, Mori S (2006) Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood. NeuroImage 29(2):493–504.  https://doi.org/10.1016/j.neuroimage.2005.08.017 CrossRefPubMedGoogle Scholar
  26. 26.
    Nelson CA 3rd, Zeanah CH, Fox NA, Marshall PJ, Smyke AT, Guthrie D (2007) Cognitive recovery in socially deprived young children: the Bucharest Early Intervention Project. Science (New York, NY) 318(5858):1937–1940.  https://doi.org/10.1126/science.1143921 CrossRefGoogle Scholar
  27. 27.
    Carmody DP, Dunn SM, Boddie-Willis AS, Kevin DeMarco J, Michael L (2004) A quantitative measure of myelination development in infants, using MR images. Neuroradiology 46(9):781–786CrossRefGoogle Scholar
  28. 28.
    Oishi K, Zilles K, Amunts K, Faria A, Jiang H, Li X, Akhter K, Hua K, Woods R, Toga AW, Pike GB, Rosa-Neto P, Evans A, Zhang J, Huang H, Miller MI, van Zijl PCM, Mazziotta J, Mori S (2008) Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. NeuroImage 43(3):447–457.  https://doi.org/10.1016/j.neuroimage.2008.07.009 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Parent ACM (1996) Carpenter’s human neuroanatomy, 9th edn. Williams & Wilkins, BaltimoreGoogle Scholar
  30. 30.
    Ball G, Srinivasan L, Aljabar P, Counsell SJ, Durighel G, Hajnal JV, Rutherford MA, Edwards AD (2013) Development of cortical microstructure in the preterm human brain. Proc Natl Acad Sci U S A 110(23):9541–9546.  https://doi.org/10.1073/pnas.1301652110 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Diagnostic RadiologyThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anPeople’s Republic of China
  2. 2.Department of Epidemiology and Health Statistics School of Public HealthXi’an Jiaotong University Health Science CenterXi’anPeople’s Republic of China
  3. 3.Department of Biomedical Engineering, School of Life Science and TechnologyXi’an Jiaotong UniversityXi’anPeople’s Republic of China

Personalised recommendations