Childhood development of brain white matter myelin: a longitudinal T1w/T2w-ratio study.
MRI
Myelin
Neurodevelopment
T1w/T2w ratio
White matter
Journal
Brain structure & function
ISSN: 1863-2661
Titre abrégé: Brain Struct Funct
Pays: Germany
ID NLM: 101282001
Informations de publication
Date de publication:
20 Nov 2023
20 Nov 2023
Historique:
received:
25
06
2023
accepted:
27
09
2023
medline:
20
11
2023
pubmed:
20
11
2023
entrez:
20
11
2023
Statut:
aheadofprint
Résumé
Myelination of human brain white matter (WM) continues into adulthood following birth, facilitating connection within and between brain networks. In vivo MRI studies using diffusion weighted imaging (DWI) suggest microstructural properties of brain WM increase over childhood and adolescence. Although DWI metrics, such as fractional anisotropy (FA), could reflect axonal myelination, they are not specific to myelin and could also represent other elements of WM microstructure, for example, fibre architecture, axon diameter and cell swelling. Little work exists specifically examining myelin development. The T1w/T2w ratio approach offers an alternative non-invasive method of estimating brain myelin. The approach uses MRI scans that are routinely part of clinical imaging and only require short acquisition times. Using T1w/T2w ratio maps from three waves of the Neuroimaging of the Children's Attention Project (NICAP) [N = 95 (208 scans); 44% female; ages 9.5-14.20 years] we aimed to investigate the developmental trajectories of brain white matter myelin in children as they enter adolescence. We also aimed to investigate whether longitudinal changes in myelination of brain WM differs between biological sex. Longitudinal regression modelling suggested non-linear increases in WM myelin brain wide. A positive parabolic, or U-shaped developmental trajectory was seen across 69 of 71 WM tracts modelled. At a corrected level, no significant effect for sex was found. These findings build on previous brain development research by suggesting that increases in brain WM microstructure from childhood to adolescence could be attributed to increases in myelin.
Identifiants
pubmed: 37982844
doi: 10.1007/s00429-023-02718-8
pii: 10.1007/s00429-023-02718-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Royal Children's Hospital Foundation
ID : RCHF 2022-1402
Organisme : National Health and Medical Research Council
ID : 1065895
Informations de copyright
© 2023. The Author(s).
Références
Andersson JL, Sotiropoulos SN (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125:1063–1078
doi: 10.1016/j.neuroimage.2015.10.019
pubmed: 26481672
Andersson JL, Skare S, Ashburner J (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20(2):870–888
doi: 10.1016/S1053-8119(03)00336-7
pubmed: 14568458
Andersson JL, Graham MS, Zsoldos E, Sotiropoulos SN (2016) Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. Neuroimage 141:556–572
doi: 10.1016/j.neuroimage.2016.06.058
pubmed: 27393418
Andersson JL, Graham MS, Drobnjak I, Zhang H, Filippini N, Bastiani M (2017) Towards a comprehensive framework for movement and distortion correction of diffusion MR images: within volume movement. Neuroimage 152:450–466
doi: 10.1016/j.neuroimage.2017.02.085
pubmed: 28284799
Arshad M, Stanley JA, Raz N (2017) Test-retest reliability and concurrent validity of in vivo myelin content indices: myelin water fraction and calibrated T(1) w/T(2) w image ratio. Hum Brain Mapp 38(4):1780–1790. https://doi.org/10.1002/hbm.23481
doi: 10.1002/hbm.23481
pubmed: 28009069
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JCJN (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3):2033–2044
doi: 10.1016/j.neuroimage.2010.09.025
pubmed: 20851191
Baranger DAA, Halchenko YO, Satz S, Ragozzino R, Iyengar S, Swartz HA, Manelis A (2021) Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio. MethodsX 8:101595. https://doi.org/10.1016/j.mex.2021.101595
doi: 10.1016/j.mex.2021.101595
pubmed: 35004227
pmcid: 8720909
Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, Jbabdi S, Andersson JL (2019) Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage 184:801–812
doi: 10.1016/j.neuroimage.2018.09.073
pubmed: 30267859
Bates D, Mächler M, Bolker B, Walker S (2014) Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:1406.5823 .
Baum GL, Flournoy JC, Glasser MF, Harms MP, Mair P, Sanders AFP, Barch DM, Buckner RL, Bookheimer S, Dapretto M, Smith S, Thomas KM, Yacoub E, Van Essen DC, Somerville LH (2022) Graded variation in T1w/T2w ratio during adolescence: measurement, caveats, and implications for development of cortical myelin. J Neurosci: off J Soc Neurosci 42(29):5681–5694. https://doi.org/10.1523/JNEUROSCI.2380-21.2022
doi: 10.1523/JNEUROSCI.2380-21.2022
Beaulieu C, Johansen-Berg H, Behrens T (2009) Diffusion MRI: from quantitative measurement to in vivo neuroanatomy. Elsevier, London
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc: Ser B (methodol) 57(1):289–300
Brauer J, Anwander A, Friederici AD (2011) Neuroanatomical prerequisites for language functions in the maturing brain. Cereb Cortex 21(2):459–466. https://doi.org/10.1093/cercor/bhq108
doi: 10.1093/cercor/bhq108
pubmed: 20566580
Buyanova IS, Arsalidou M (2021) Cerebral white matter myelination and relations to age, gender, and cognition: a selective review. Front Human Neurosci. https://doi.org/10.3389/fnhum.2021.662031
doi: 10.3389/fnhum.2021.662031
Chen B, Linke A, Olson L, Kohli J, Kinnear M, Sereno M, Müller R-A, Carper R, Fishman I (2022) Cortical myelination in toddlers and preschoolers with autism spectrum disorder. Dev Neurobiol 82(3):261–274. https://doi.org/10.1002/dneu.22874
doi: 10.1002/dneu.22874
pubmed: 35348301
pmcid: 9325547
Cordero-Grande L, Christiaens D, Hutter J, Price AN, Hajnal JV (2019) Complex diffusion-weighted image estimation via matrix recovery under general noise models. Neuroimage 200:391–404
doi: 10.1016/j.neuroimage.2019.06.039
pubmed: 31226495
Dhollander T, Mito R, Raffelt D, Connelly A (2019) Improved white matter response function estimation for 3-tissue constrained spherical deconvolution. Proc Intl Soc Mag Reson Med. https://www.researchgate.net/publication/331165168_Improved_white_matter_response_function_estimation_for_3-tissue_constrained_spherical_deconvolution
Du G, Lewis MM, Sica C, Kong L, Huang X (2019) Magnetic resonance T1w/T2w ratio: a parsimonious marker for Parkinson disease. Ann Neurol 85(1):96–104. https://doi.org/10.1002/ana.25376
doi: 10.1002/ana.25376
pubmed: 30408230
Fonov VS, Evans AC, McKinstry RC, Almli C, Collins DJN (2009) Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47:S102
doi: 10.1016/S1053-8119(09)70884-5
Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, Neuroimage BDCGJ (2011) Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1):313–327
doi: 10.1016/j.neuroimage.2010.07.033
pubmed: 20656036
Ganzetti M, Wenderoth N, Mantini D (2015) Mapping pathological changes in brain structure by combining T1- and T2-weighted MR imaging data. Neuroradiology 57(9):917–928. https://doi.org/10.1007/s00234-015-1550-4
doi: 10.1007/s00234-015-1550-4
pubmed: 26104102
pmcid: 4572060
Genc S, Smith RE, Malpas CB, Anderson V, Nicholson JM, Efron D, Sciberras E, Seal ML, Silk TJ (2018) Development of white matter fibre density and morphology over childhood: a longitudinal fixel-based analysis. Neuroimage 183:666–676. https://doi.org/10.1016/j.neuroimage.2018.08.043
doi: 10.1016/j.neuroimage.2018.08.043
pubmed: 30142448
Genc S, Malpas CB, Gulenc A, Sciberras E, Efron D, Silk TJ, Seal ML (2020) Longitudinal patterns of white matter fibre density and morphology in children are associated with age and pubertal stage. Dev Cogn Neurosci 45:100853. https://doi.org/10.1016/j.dcn.2020.100853
doi: 10.1016/j.dcn.2020.100853
pubmed: 32932204
pmcid: 7498759
Glasser MF, Van Essen DC (2011) Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI. J Neurosci 31(32):11597–11616
doi: 10.1523/JNEUROSCI.2180-11.2011
pubmed: 21832190
pmcid: 3167149
Haist F, Adamo M, Han J, Lee K, Stiles J (2011) On the development of human face-processing abilities: Evidence for hyperactivation of the extended face system in children. J vis 11(11):461–461. https://doi.org/10.1167/11.11.461
doi: 10.1167/11.11.461
Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156
doi: 10.1016/S1361-8415(01)00036-6
pubmed: 11516708
Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2):825–841
doi: 10.1006/nimg.2002.1132
pubmed: 12377157
Kellner E, Dhital B, Kiselev VG, Reisert M (2016) Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 76(5):1574–1581
doi: 10.1002/mrm.26054
pubmed: 26745823
Lebel C, Beaulieu C (2011) Longitudinal development of human brain wiring continues from childhood into adulthood. J Neurosci 31(30):10937. https://doi.org/10.1523/JNEUROSCI.5302-10.2011
doi: 10.1523/JNEUROSCI.5302-10.2011
pubmed: 21795544
pmcid: 6623097
Lebel C, Deoni SCL (2018) The development of brain white matter microstructure. Neuroimage. https://doi.org/10.1016/j.neuroimage.2017.12.097
doi: 10.1016/j.neuroimage.2017.12.097
pubmed: 30189341
Lewis F, Butler A, Gilbert L (2011) A unified approach to model selection using the likelihood ratio test. Methods Ecol Evol 2(2):155–162
doi: 10.1111/j.2041-210X.2010.00063.x
Luo X, Li K, Zeng Q, Huang P, Jiaerken Y, Wang S, Shen Z, Xu X, Xu J, Wang C, Kong L, Zhou J, Zhang M (2019) Application of T1-/T2-weighted ratio mapping to elucidate intracortical demyelination process in the Alzheimer’s disease continuum. Front Neurosci 13:904. https://doi.org/10.3389/fnins.2019.00904
doi: 10.3389/fnins.2019.00904
pubmed: 31551678
pmcid: 6748350
May T, Adesina I, McGillivray J, Rinehart NJ (2019) Sex differences in neurodevelopmental disorders. Curr Opin Neurol 32(4):622–626
doi: 10.1097/WCO.0000000000000714
pubmed: 31135460
Norbom LB, Rokicki J, Alnaes D, Kaufmann T, Doan NT, Andreassen OA, Westlye LT, Tamnes CK (2020) Maturation of cortical microstructure and cognitive development in childhood and adolescence: a T1w/T2w ratio MRI study. Hum Brain Mapp 41(16):4676–4690. https://doi.org/10.1002/hbm.25149
doi: 10.1002/hbm.25149
pubmed: 32744409
pmcid: 7555087
Patel Y, Shin J, Drakesmith M, Evans J, Pausova Z, Paus T (2020) Virtual histology of multi-modal magnetic resonance imaging of cerebral cortex in young men. Neuroimage 218:116968. https://doi.org/10.1016/j.neuroimage.2020.116968
doi: 10.1016/j.neuroimage.2020.116968
pubmed: 32450248
Petracca M, El Mendili MM, Moro M, Cocozza S, Podranski K, Fleysher L, Inglese M (2020) Laminar analysis of the cortical T1/T2-weighted ratio at 7T. Neurol(r) Neuroimmunol Neuroinflamm. https://doi.org/10.1212/NXI.0000000000000900
doi: 10.1212/NXI.0000000000000900
Pinheiro J, Bates D, DebRoy S, Sarkar D, Heisterkamp S, Van Willigen B, Maintainer R (2017) Package ‘nlme.’ Linear Nonlinear Mixed Eff Models Version 3(1):274
Preziosa P, Bouman PM, Kiljan S, Steenwijk MD, Meani A, Pouwels PJ, Rocca MA, Filippi M, Geurts JJG, Jonkman LE (2021) Neurite density explains cortical T1-weighted/T2-weighted ratio in multiple sclerosis. J Neurol Neurosurg Psychiatry 92(7):790–792. https://doi.org/10.1136/jnnp-2020-324391
doi: 10.1136/jnnp-2020-324391
pubmed: 33436500
R Core Team R (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/
Raffelt DA, Tournier JD, Smith RE, Vaughan DN, Jackson G, Ridgway GR, Connelly A (2017) Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage 144:58–73. https://doi.org/10.1016/j.neuroimage.2016.09.029
doi: 10.1016/j.neuroimage.2016.09.029
pubmed: 27639350
Silk TJ, Genc S, Anderson V, Efron D, Hazell P, Nicholson JM, Kean M, Malpas CB, Sciberras E (2016) Developmental brain trajectories in children with ADHD and controls: a longitudinal neuroimaging study. BMC Psychiatry 16(1):59. https://doi.org/10.1186/s12888-016-0770-4
doi: 10.1186/s12888-016-0770-4
pubmed: 26969310
pmcid: 4787204
Simmonds DJ, Hallquist MN, Asato M, Luna B (2014) Developmental stages and sex differences of white matter and behavioral development through adolescence: a longitudinal diffusion tensor imaging (DTI) study. Neuroimage 92:356–368. https://doi.org/10.1016/j.neuroimage.2013.12.044
doi: 10.1016/j.neuroimage.2013.12.044
pubmed: 24384150
Smith SM (2002) Fast robust automated brain extraction. Human Brain Mapp 17(3):143–155
doi: 10.1002/hbm.10062
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:S208–S219
doi: 10.1016/j.neuroimage.2004.07.051
pubmed: 15501092
Smith RE, Tournier J-D, Calamante F, Connelly A (2013) SIFT: Spherical-deconvolution informed filtering of tractograms. Neuroimage 67:298–312
doi: 10.1016/j.neuroimage.2012.11.049
pubmed: 23238430
Soun JE, Liu MZ, Cauley KA, Grinband J (2017) Evaluation of neonatal brain myelination using the T1- and T2-weighted MRI ratio. J Magn Reson Imaging: JMRI 46(3):690–696. https://doi.org/10.1002/jmri.25570
doi: 10.1002/jmri.25570
pubmed: 28019046
Tamnes CK, Roalf DR, Goddings A-L, Lebel C (2018) Diffusion MRI of white matter microstructure development in childhood and adolescence: methods, challenges and progress. Dev Cognit Neurosci 33:161–175. https://doi.org/10.1016/j.dcn.2017.12.002
doi: 10.1016/j.dcn.2017.12.002
Thiebaut de Schotten M, Dell’Acqua F, Valabregue R, Catani M (2012) Monkey to human comparative anatomy of the frontal lobe association tracts. Cortex 48(1):82–96. https://doi.org/10.1016/j.cortex.2011.10.001
doi: 10.1016/j.cortex.2011.10.001
pubmed: 22088488
Thompson DK, Yang JYM, Chen J, Kelly CE, Adamson CL, Alexander B, Gilchrist C, Matthews LG, Lee KJ, Hunt RW, Cheong JLY, Spencer-Smith M, Neil JJ, Seal ML, Inder TE, Doyle LW, Anderson PJ (2022) Brain white matter development over the first 13 years in very preterm and typically developing children based on the T1-w/T2-w ratio. Neurology 98(9):e924. https://doi.org/10.1212/WNL.0000000000013250
doi: 10.1212/WNL.0000000000013250
pubmed: 34937788
pmcid: 8901175
Tournier J-D, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, Christiaens D, Jeurissen B, Yeh C-H, Connelly A (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202:116137
doi: 10.1016/j.neuroimage.2019.116137
pubmed: 31473352
Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320
doi: 10.1109/TMI.2010.2046908
pubmed: 20378467
pmcid: 3071855
Uddin MN, Figley TD, Solar KG, Shatil AS, Figley CR (2019) Comparisons between multi-component myelin water fraction, T1w/T2w ratio, and diffusion tensor imaging measures in healthy human brain structures. Sci Rep 9(1):2500. https://doi.org/10.1038/s41598-019-39199-x
doi: 10.1038/s41598-019-39199-x
pubmed: 30792440
pmcid: 6384876
Veraart J, Fieremans E, Novikov DS (2016a) Diffusion MRI noise mapping using random matrix theory. Magn Reson Med 76(5):1582–1593
doi: 10.1002/mrm.26059
pubmed: 26599599
Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E (2016b) Denoising of diffusion MRI using random matrix theory. Neuroimage 142:394–406
doi: 10.1016/j.neuroimage.2016.08.016
pubmed: 27523449
Wasserthal J, Neher P, Maier-Hein KHJN (2018) Tractseg-fast and accurate white matter tract segmentation. Neuroimage 183:239–253
doi: 10.1016/j.neuroimage.2018.07.070
pubmed: 30086412
Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45–57. https://doi.org/10.1109/42.906424
doi: 10.1109/42.906424
pubmed: 11293691