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

  • Paediatric Neuroradiology
  • Published:
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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.

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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

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Author information

Author notes

  1. CL and CJ contributed equally to this work.

Authors and Affiliations

  1. Department of Diagnostic Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China

    Congcong Liu, Chao Jin, Zhijie Jian, Miaomiao Wang, Xianjun Li, Heng Liu, Qinli Sun & Jian Yang

  2. Department of Epidemiology and Health Statistics School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, 710054, People’s Republic of China

    Lingxia Zeng

  3. Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710054, People’s Republic of China

    Jian Yang

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  1. Congcong Liu
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Corresponding author

Correspondence to Jian Yang.

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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.

Electronic supplementary material

Supplementary Fig. 1

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

High resolution image (TIF 17618 kb)

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)

High resolution image (TIF 19554 kb)

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)

High resolution image (TIF 21073 kb)

ESM 1

(DOCX 38 kb)

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Liu, C., Jin, C., Jian, Z. et al. Assessment of myelination progression in subcortical white matter of children aged 6–48 months using T2-weighted imaging. Neuroradiology 60, 1343–1351 (2018). https://doi.org/10.1007/s00234-018-2108-z

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