Development of Myelin Growth Charts of the White Matter Using T1 Relaxometry.


Journal

AJNR. American journal of neuroradiology
ISSN: 1936-959X
Titre abrégé: AJNR Am J Neuroradiol
Pays: United States
ID NLM: 8003708

Informations de publication

Date de publication:
18 Jul 2024
Historique:
received: 29 11 2023
accepted: 02 04 2024
medline: 19 7 2024
pubmed: 19 7 2024
entrez: 18 7 2024
Statut: aheadofprint

Résumé

Myelin maturation occurs in late fetal life to early adulthood, with the most rapid changes observed in the first few years of infancy. To quantify the degree of myelination, a specific MR imaging sequence is required to measure the changes in tissue proton relaxivity (R1). R1 positively correlates with the degree of myelination maturation at a given age. Similar to head circumference charts, these data can be used to develop normal growth charts for specific white matter tracts to detect pathologies involving abnormal myelination. This is a cross-sectional study using normal clinical pediatric brain MR images with the MP2RAGE sequence to generate T1 maps. The T1 maps were segmented to 75 brain regions from a brain atlas (white matter and gyri). Statistical modeling for all subjects across regions and the age range was computed, and estimates of population-level percentile ranking were computed to describe the effective myelination rate as a function of age. Test-retest analysis was performed to assess reproducibility. Logistic trendline and regression were performed for selected white matter regions and plotted for growth charts. After exclusion for abnormal MR imaging or diseases affecting myelination from the electronic medical record, 103 subject MR images were included, ranging from birth to 17 years of age. Test-retest analysis resulted in a high correlation for white matter ( These data can serve as a myelination growth chart to permit patient comparisons with normal levels with respect to age and brain regions, thus improving detection of developmental disorders affecting myelin.

Sections du résumé

BACKGROUND AND PURPOSE OBJECTIVE
Myelin maturation occurs in late fetal life to early adulthood, with the most rapid changes observed in the first few years of infancy. To quantify the degree of myelination, a specific MR imaging sequence is required to measure the changes in tissue proton relaxivity (R1). R1 positively correlates with the degree of myelination maturation at a given age. Similar to head circumference charts, these data can be used to develop normal growth charts for specific white matter tracts to detect pathologies involving abnormal myelination.
MATERIALS AND METHODS METHODS
This is a cross-sectional study using normal clinical pediatric brain MR images with the MP2RAGE sequence to generate T1 maps. The T1 maps were segmented to 75 brain regions from a brain atlas (white matter and gyri). Statistical modeling for all subjects across regions and the age range was computed, and estimates of population-level percentile ranking were computed to describe the effective myelination rate as a function of age. Test-retest analysis was performed to assess reproducibility. Logistic trendline and regression were performed for selected white matter regions and plotted for growth charts.
RESULTS RESULTS
After exclusion for abnormal MR imaging or diseases affecting myelination from the electronic medical record, 103 subject MR images were included, ranging from birth to 17 years of age. Test-retest analysis resulted in a high correlation for white matter (
CONCLUSIONS CONCLUSIONS
These data can serve as a myelination growth chart to permit patient comparisons with normal levels with respect to age and brain regions, thus improving detection of developmental disorders affecting myelin.

Identifiants

pubmed: 39025639
pii: ajnr.A8306
doi: 10.3174/ajnr.A8306
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024 by American Journal of Neuroradiology.

Auteurs

Chang Y Ho (CY)

From the Department of Radiology and Imaging Sciences (C.Y.H., P.R.T.), Indiana University School of Medicine, Indianapolis, Indiana cyho@iu.edu.

Scott Persohn (S)

Department of Medicine (S.P., M.S., P.R.T.), Indiana University School of Medicine, Indianapolis, Indiana.

Meghana Sankar (M)

Department of Medicine (S.P., M.S., P.R.T.), Indiana University School of Medicine, Indianapolis, Indiana.

Paul R Territo (PR)

From the Department of Radiology and Imaging Sciences (C.Y.H., P.R.T.), Indiana University School of Medicine, Indianapolis, Indiana.
Department of Medicine (S.P., M.S., P.R.T.), Indiana University School of Medicine, Indianapolis, Indiana.
Stark Neuroscience Research Institute (P.R.T.), Indiana University School of Medicine, Indianapolis, Indiana.

Classifications MeSH