Brain Myelin Water Fraction and Diffusion Tensor Imaging Atlases for 9-10 Year-Old Children.
Atlas
diffusion tensor imaging
myelin water fraction
myelin water imaging
pediatric
Journal
Journal of neuroimaging : official journal of the American Society of Neuroimaging
ISSN: 1552-6569
Titre abrégé: J Neuroimaging
Pays: United States
ID NLM: 9102705
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
17
09
2019
revised:
18
12
2019
accepted:
17
01
2020
pubmed:
18
2
2020
medline:
15
12
2020
entrez:
18
2
2020
Statut:
ppublish
Résumé
Myelin water imaging (MWI) and diffusion tensor imaging (DTI) provide information about myelin and axon-related brain microstructure, which can be useful for investigating normal brain development and many childhood brain disorders. While pediatric DTI atlases exist, there are no pediatric MWI atlases available for the 9-10 years old age group. As myelination and structural development occurs throughout childhood and adolescence, studies of pediatric brain pathologies must use age-specific MWI and DTI healthy control data. We created atlases of myelin water fraction (MWF) and DTI metrics for healthy children aged 9-10 years for use as normative data in pediatric neuroimaging studies. 3D-T Adults had significantly higher MWF than the pediatric cohort in seven of the nine white matter ROIs, but not in the genu of the corpus callosum or the cingulum. In the pediatric data, MWF correlated significantly with mean diffusivity, but not with axial diffusivity, radial diffusivity, or fractional anisotropy. Normative MWF and DTI metrics from a group of 9-10 year old healthy children provide a resource for comparison to pathologies. The age-specific atlases are ready for use in pediatric neuroimaging research and can be accessed: https://sourceforge.net/projects/pediatric-mri-myelin-diffusion/.
Sections du résumé
BACKGROUND AND PURPOSE
Myelin water imaging (MWI) and diffusion tensor imaging (DTI) provide information about myelin and axon-related brain microstructure, which can be useful for investigating normal brain development and many childhood brain disorders. While pediatric DTI atlases exist, there are no pediatric MWI atlases available for the 9-10 years old age group. As myelination and structural development occurs throughout childhood and adolescence, studies of pediatric brain pathologies must use age-specific MWI and DTI healthy control data. We created atlases of myelin water fraction (MWF) and DTI metrics for healthy children aged 9-10 years for use as normative data in pediatric neuroimaging studies.
METHODS
3D-T
RESULTS
Adults had significantly higher MWF than the pediatric cohort in seven of the nine white matter ROIs, but not in the genu of the corpus callosum or the cingulum. In the pediatric data, MWF correlated significantly with mean diffusivity, but not with axial diffusivity, radial diffusivity, or fractional anisotropy.
CONCLUSIONS
Normative MWF and DTI metrics from a group of 9-10 year old healthy children provide a resource for comparison to pathologies. The age-specific atlases are ready for use in pediatric neuroimaging research and can be accessed: https://sourceforge.net/projects/pediatric-mri-myelin-diffusion/.
Substances chimiques
Water
059QF0KO0R
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
150-160Subventions
Organisme : CIHR
Pays : Canada
Informations de copyright
© 2020 by the American Society of Neuroimaging.
Références
Kostović I, Jovanov-Milošević N. The development of cerebral connections during the first 20-45 weeks’ gestation. Semin Fetal Neonatal Med 2006;11:415-22.
Benes FM, Turtle M, Khan Y, et al. Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood. Arch Gen Psychiatry 1994;51:477-84.
O'Muircheartaigh J, Dean DC, Ginestet CE, et al. White matter development and early cognition in babies and toddlers. Hum Brain Mapp 2014;35:4475-87.
Chevalier N, Kurth S, Doucette MR, et al. Myelination is associated with processing speed in early childhood: preliminary insights. PLoS One 2015;10:e0139897.
Deoni SCL, O'Muircheartaigh J, Elison JT, et al. White matter maturation profiles through early childhood predict general cognitive ability. Brain Struct Funct 2016;221:1189-203.
Whitaker KJ, Kolind SH, MacKay AL, et al. Quantifying development: investigating highly myelinated voxels in preadolescent corpus callosum. Neuroimage 2008;43:731-5.
Paus T. Growth of white matter in the adolescent brain: myelin or axon? Brain Cogn 2010;72:26-35.
Giedd JN, Blumenthal J, Jeffries NO, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 1999;2:861-3.
Lebel C, Beaulieu C. Longitudinal development of human brain wiring continues from childhood into adulthood. J Neurosci 2011;31:10937-47.
Yoon U, Fonov VS, Perusse D, et al. The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 2009;45:769-77.
Richards JE, Sanchez C, Phillips-Meek M, et al. A database of age-appropriate average MRI templates. Neuroimage 2016;124:1254-9.
Basser PJ, Mattiello J, Lebihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994;103:247-54.
Landman BA, Huang AJ, Gifford A, et al. Multi-parametric neuroimaging reproducibility: a 3T resource study. Neuroimage 2011;54:2854-66.
Sadeghi N, Prastawa M, Fletcher PT, et al. Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain. Neuroimage 2013;68:236-47.
Faria AV, Zhang J, Oishi K, et al. Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection. Neuroimage 2010;52:415-28.
Hermoye L, Saint-Martin C, Cosnard G, et al. Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood. Neuroimage 2006;29:493-504.
Matejko AA, Ansari D. Drawing connections between white matter and numerical and mathematical cognition: a literature review. Neurosci Biobehav Rev 2015;48:35-52.
Lebel C, Shaywitz B, Holahan J, et al. Diffusion tensor imaging correlates of reading ability in dysfluent and non-impaired readers. Brain Lang 2013;125:215-22.
Beaulieu C. The basis of anisotropic water diffusion in the nervous system-a technical review. NMR Biomed 2002;15:435-55.
Beaulieu C, Allen PS. Determinants of anisotropic water diffusion in nerves. Magn Reson Med 1994;31:394-400.
Mori S, Oishi K, Jiang H, et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 2008;40:570-82.
Van Hecke W, Sijbers J, D'Agostino E, et al. On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain. Neuroimage 2008;43:69-80.
Peng H, Orlichenko A, Dawe RJ, et al. Development of a human brain diffusion tensor template. Neuroimage 2009;46:967-80.
Zhang S, Peng H, Dawe RJ, et al. Enhanced ICBM diffusion tensor template of the human brain. NeuroImage 2011;54:974-84.
Walker L, Chang L-C, Nayak A, et al. The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI). Neuroimage 2016;124:1125-30.
Verhoeven JS, Sage CA, Leemans A, et al. Construction of a stereotaxic DTI atlas with full diffusion tensor information for studying white matter maturation from childhood to adolescence using tractography-based segmentations. Hum Brain Mapp 2010;31:470-86.
Mackay A, Whittall K, Adler J, et al. In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med 1994;31:673-7.
Whittall KP, Mackay A, Graeb DA, et al. In vivo measurement of T2 distributions and water contents in normal human brain. Magn Reson Med 1997;37:34-43.
Laule C, Vavasour IM, Kolind SH, et al. Magnetic resonance imaging of myelin. Neurother 2007;4:460-84.
Laule C, Leung E, Li D, et al. Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult Scler J 2006;12:747-53.
Laule C, Kozlowski P, Leung E, et al. Myelin water imaging of multiple sclerosis at 7T: correlations with histopathology. Neuroimage 2008;40:1575-80.
Laule C, Yung A, Pavolva V, et al. High-resolution myelin water imaging in post-mortem multiple sclerosis spinal cord: a case report. Mult Scler 2016;22:1485-9.
Meyers SM, Vavasour IM, Maedler B, et al. Multicenter measurements of myelin water fraction and geometric mean T2: intra-and inter-site reproducibility. J Magn Reson Imaging 2013;38:1445-53.
Laule C, Vavasour IM, Moore GRW, et al. Water content and myelin water fraction in multiple sclerosis. J Neurol 2004;251:284-93.
Laule C, Vavasour IM, Zhao Y, et al. Two-year study of cervical cord volume and myelin water in primary progressive multiple sclerosis. Mult Scler 2010;16:670-7.
Vargas WS, Monohan E, Pandya S, et al. Measuring longitudinal myelin water fraction in new multiple sclerosis lesions. Neuroimage Clin 2015;9:369-75.
Vavasour IM, Laule C, Li DKB, et al. Longitudinal changes in myelin water fraction in two MS patients with active disease. J Neurol Sci 2009;276:49-53.
Faizy TD, Thaler C, Kumar D, et al. Heterogeneity of multiple sclerosis lesions in multislice myelin water imaging. PLoS One 2016;11:e0151496.
Dvorak AV, Ljungberg E, Vavasour IM, et al. Rapid myelin water imaging for the assessment of cervical spinal cord myelin damage. Neuroimage Clin 2019;23:101896.
Jeong IH, Choi JY, Kim S-H, et al. Comparison of myelin water fraction values in periventricular white matter lesions between multiple sclerosis and neuromyelitis optica spectrum disorder. Mult Scler J 2016;22:1616-20.
Lang DJM, Yip E, MacKay AL, et al. 48 echo T2 myelin imaging of white matter in first-episode schizophrenia: evidence for aberrant myelination. Neuroimage Clin 2014;6:408-14.
Deoni SCL, Zinkstok JR, Daly E, et al. White-matter relaxation time and myelin water fraction differences in young adults with autism. Psychol Med 2015;45:795-805.
Davies-Thompson J, Vavasour I, Scheel M, et al. Reduced myelin water in the white matter tracts of patients with Niemann-Pick disease type C. AJNR 2016;37:1487-9.
Wright AD, Jarrett M, Vavasour I, et al. Myelin water fraction is transiently reduced after a single mild traumatic brain injury-a prospective cohort study in collegiate hockey players. PLoS One 2016;11:e0150215.
Laule C, Vavasour IM, Shahinfard E, et al. Hematopoietic stem cell transplantation in late-onset Krabbe disease: no evidence of worsening demyelination and axonal loss 4 years post-allograft. J Neuroimaging 2018;28:252-5.
Borich MR, Mackay AL, Vavasour IM, et al. Evaluation of white matter myelin water fraction in chronic stroke. Neuroimage Clin 2013;2:569-80.
McLachlan K, Vavasour I, MacKay A, et al. Myelin water fraction imaging of the brain in children with prenatal alcohol exposure. Alcohol Clin Exp Res 2019;43:833-41.
Deoni SCL, Mercure E, Blasi A, et al. Mapping infant brain myelination with magnetic resonance imaging. J Neurosci 2011;31:784-91.
Deoni SCL, Dean DC, O'Muircheartaigh J, et al. Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping. Neuroimage 2012;63:1038-53.
Dean DC, O'Muircheartaigh J, Dirks H, et al. Modeling healthy male white matter and myelin development: 3 through 60 months of age. Neuroimage 2014;84:742-52.
Dean DC, O'Muircheartaigh J, Dirks H, et al. Estimating the age of healthy infants from quantitative myelin water fraction maps. Hum Brain Mapp 2015;36:1233-44.
Dean DC, O'Muircheartaigh J, Dirks H, et al. Characterizing longitudinal white matter development during early childhood. Brain Struct Funct 2015;220:1921-33.
Geeraert BL, Lebel RM, Mah AC, et al. A comparison of inhomogeneous magnetization transfer, myelin volume fraction, and diffusion tensor imaging measures in healthy children. Neuroimage 2018;182:343-50.
Liu H, Rubino C, Dvorak AV, et al. Myelin water atlas: a template for myelin distribution in the brain. J Neuroimaging 2019;29:699-706.
Liu H, Ljungberg E, Dvorak AV, et al. Myelin water fraction and intra/extracellular water geometric mean T2 normative atlases for the cervical spinal cord from 3T MRI. J Neuroimaging 2019;30(1):50-57.
Avants BB, Epstein CL, Grossman M, et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 2008;12:26-41.
Avants BB, Tustison NJ, Song G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 2011;54:2033-44.
Jenkinson M, Bannister P, Brady M, et al. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17:825-41.
Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5:143-56.
Lawson GM, Duda JT, Avants BB, et al. Associations between children's socioeconomic status and prefrontal cortical thickness. Dev Sci 2013;16:641-52.
Avants BB, Yushkevich P, Pluta J, et al. The optimal template effect in hippocampus studies of diseased populations. Neuroimage 2010;49:2457-66.
Avants BB, Duda JT, Kilroy E, et al. The pediatric template of brain perfusion. Sci Data 2015;2:150003.
Prasloski T, Mädler B, Xiang Q-S, et al. Applications of stimulated echo correction to multicomponent T2 analysis. Magn Reson Med 2012;67:1803-14.
Yoo Y, Tam R. Non-local spatial regularization of MRI T2 relaxation images for myelin water quantification. MICCAI 2013:614-21.
Behrens TEJ, Woolrich MW, Jenkinson M, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 2003;50:1077-88.
Behrens TEJ, Berg HJ, Jbabdi S, et al. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 2007;34:144-55.
Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010;29:1310-20.
Mori S, Wakana S, van Zijl PCM, et al. MRI atlas of human white matter. Amsterdam, Netherlands: Elsevier; 2005.
Wakana S, Caprihan A, Panzenboeck MM, et al. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 2007;36:630-44.
Hua K, Zhang J, Wakana S, et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 2008;39:336-47.
Jenkinson M, Beckmann CF, Behrens TEJ, et al. FSL. Neuroimage 2012;62:782-90.
Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23(Suppl 1):S208-19.
Woolrich MW, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage 2009;45(Suppl 1):S173-86.
Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001;20:45-57.
Zhang J, Kolind SH, Laule C, et al. Comparison of myelin water fraction from multi-echo T2 decay curve and steady-state methods. Magn Reson Med 2015;73:223-32.
Harkins KD, Dula AN, Does MD. Effect of intercompartmental water exchange on the apparent myelin water fraction in multiexponential T2 measurements of rat spinal cord. Magn Reson Med 2012;67:793-800.
Zhang J, Kolind SH, Laule C, et al. How does magnetization transfer influence mcDESPOT results? Magn Reson Med 2015;74:1327-35.
West DJ, Teixeira RPAG, Wood TC, et al. Inherent and unpredictable bias in multi-component DESPOT myelin water fraction estimation. Neuroimage 2019;195:78-88.
Russell-Schulz B, Laule C, Li DKB, et al. What causes the hyperintense T2-weighting and increased short T2 signal in the corticospinal tract? Magn Reson Imaging 2013;31:329-35.
Brody BA, Kinney HC, Kloman AS, et al. Sequence of central nervous system myelination in human infancy. I. An autopsy study of myelination. J Neuropathol Exp Neurol 1987;46:283-301.
Zhang J, Vavasour I, Kolind S, et al. Advanced myelin water imaging techniques for rapid data acquisition and long T2 component measurements. Proceedings of the 23rd Annual Meeting of ISMRM; Toronto, Ontario, Canada. 2015; 23.
Mädler B, Drabycz SA, Kolind SH, et al. Is diffusion anisotropy an accurate monitor of myelination? Magn Reson Imaging 2008;26:874-88.
Drenthen GS, Backes WH, Aldenkamp AP, et al. Applicability and reproducibility of 2D multi-slice GRASE myelin water fraction with varying acquisition acceleration. Neuroimage 2019;195:333-9.
Prasloski T, Rauscher A, MacKay AL, et al. Rapid whole cerebrum myelin water imaging using a 3D GRASE sequence. Neuroimage 2012;63:533-9.