Normative volumes and relaxation times at 3T during brain development.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
25 Apr 2024
25 Apr 2024
Historique:
received:
19
10
2023
accepted:
16
04
2024
medline:
26
4
2024
pubmed:
26
4
2024
entrez:
25
4
2024
Statut:
epublish
Résumé
While research has unveiled and quantified brain markers of abnormal neurodevelopment, clinicians still work with qualitative metrics for MRI brain investigation. The purpose of the current article is to bridge the knowledge gap between case-control cohort studies and individual patient care. Here, we provide a unique dataset of seventy-three 3-to-17 years-old healthy subjects acquired with a 6-minute MRI protocol encompassing T1 and T2 relaxation quantitative sequence that can be readily implemented in the clinical setting; MP2RAGE for T1 mapping and the prototype sequence GRAPPATINI for T2 mapping. White matter and grey matter volumes were automatically quantified. We further provide normative developmental curves based on these two imaging sequences; T1, T2 and volume normative ranges with respect to age were computed, for each ROI of a pediatric brain atlas. This open-source dataset provides normative values allowing to position individual patients acquired with the same protocol on the brain maturation curve and as such provides potentially useful quantitative biomarkers facilitating precise and personalized care.
Identifiants
pubmed: 38664431
doi: 10.1038/s41597-024-03267-3
pii: 10.1038/s41597-024-03267-3
doi:
Types de publication
Dataset
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
429Informations de copyright
© 2024. The Author(s).
Références
Baum, G. L. et al. Development of structure-function coupling in human brain networks during youth. Proc. Natl. Acad. Sci. USA 117, 771–778 (2020).
doi: 10.1073/pnas.1912034117
pubmed: 31874926
Giedd, J. N. et al. Child psychiatry branch of the national institute of mental health longitudinal structural magnetic resonance imaging study of human brain development. Neuropsychopharmacology 40, 43–49 (2015).
doi: 10.1038/npp.2014.236
pubmed: 25195638
Váša, F. et al. An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome. NeuroImage Clin. 10, 239–249 (2016).
doi: 10.1016/j.nicl.2015.11.017
pubmed: 26870660
Hensch, T. K. Critical period regulation. Annu. Rev. Neurosci. 27, 549–579 (2004).
doi: 10.1146/annurev.neuro.27.070203.144327
pubmed: 15217343
Posner, M. I., Rothbart, M. K., Sheese, B. E. & Tang, Y. The anterior cingulate gyrus and the mechanism of self-regulation. Cogn. Affect. Behav. Neurosci. 7, 391–395 (2007).
doi: 10.3758/CABN.7.4.391
pubmed: 18189012
Takesian, A. E. & Hensch, T. K. Balancing plasticity/stability across brain development. Prog. Brain Res. 207, 3–34 (2013).
doi: 10.1016/B978-0-444-63327-9.00001-1
pubmed: 24309249
Voytek, B. & Knight, R. T. Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biol. Psychiatry 77, 1089–1097 (2015).
doi: 10.1016/j.biopsych.2015.04.016
pubmed: 26005114
pmcid: 4443259
Lenroot, R. K. & Giedd, J. N. Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neurosci. Biobehav. Rev. 30, 718–729 (2006).
doi: 10.1016/j.neubiorev.2006.06.001
pubmed: 16887188
Durston, S. et al. Anatomical MRI of the developing human brain: what have we learned? J. Am. Acad. Child Adolesc. Psychiatry 40, 1012–1020 (2001).
doi: 10.1097/00004583-200109000-00009
pubmed: 11556624
Paquette, N., Gajawelli, N. & Lepore, N. Structural neuroimaging. Handb. Clin. Neurol. 174, 251–264 (2020).
doi: 10.1016/B978-0-444-64148-9.00018-1
pubmed: 32977882
Shaw, P., Gogtay, N. & Rapoport, J. Childhood psychiatric disorders as anomalies in neurodevelopmental trajectories. Hum. Brain Mapp. 31, 917–925 (2010).
doi: 10.1002/hbm.21028
pubmed: 20496382
pmcid: 6870870
Fischi-Gómez, E. et al. Structural brain connectivity in school-age preterm infants provides evidence for impaired networks relevant for higher order cognitive skills and social cognition. Cereb. Cortex 25, 2793–2805 (2015).
doi: 10.1093/cercor/bhu073
pubmed: 24794920
Fleiss, B., Gressens, P. & Stolp, H. B. Cortical gray matter injury in encephalopathy of prematurity: Link to neurodevelopmental disorders. Front. Neurol. 11, 575 (2020).
doi: 10.3389/fneur.2020.00575
pubmed: 32765390
pmcid: 7381224
Mathur, A. & Inder, T. Magnetic resonance imaging–insights into brain injury and outcomes in premature infants. J. Commun. Disord. 42, 248–255 (2009).
doi: 10.1016/j.jcomdis.2009.03.007
pubmed: 19406431
pmcid: 3553556
Denervaud, S. et al. Structural brain abnormalities in epilepsy with myoclonic atonic seizures. Epilepsy Res. 177, 106771 (2021).
doi: 10.1016/j.eplepsyres.2021.106771
pubmed: 34562678
Hasan, K. M., Walimuni, I. S., Kramer, L. A. & Frye, R. E. Human brain atlas-based volumetry and relaxometry: application to healthy development and natural aging. Magn Reson Med 65, 382–1389 (2010).
Romascano, D. et al. Developmental relaxometry 2023. OpenNeuro.org https://doi.org/10.18112/openneuro.ds004611.v1.0.0 (2024).
Piredda, G. F., Hilbert, T., Thiran, J.-P. & Kober, T. Probing myelin content of the human brain with mri: A review. Magnetic resonance in medicine 85, 627–652 (2021).
doi: 10.1002/mrm.28509
pubmed: 32936494
Hilbert, T. et al. Accelerated T2 mapping combining parallel MRI and model based reconstruction: GRAPPATINI. J. Magn. Reson. Imaging 48, 359–368 (2018).
doi: 10.1002/jmri.25972
pubmed: 29446508
Bonnier, G., Maréchal, B., Marques, J. P., Thiran, J.-P. & Granziera, C. The combined quantification and interpretation of multiple quantitative magnetic resonance imaging metrics enlightens longitudinal changes compatible with brain repair in relapsing-remitting multiple sclerosis patients. Frontiers in neurology 8, 280106 (2017).
doi: 10.3389/fneur.2017.00506
Bonnier, G. et al. Advanced mri unravels the nature of tissue alterations in early multiple sclerosis. Annals of clinical and translational neurology 1, 423–432 (2014).
doi: 10.1002/acn3.68
pubmed: 25356412
pmcid: 4184670
Vietti Violi, N. et al. Patient respiratory-triggered quantitative t2 mapping in the pancreas. Journal of Magnetic Resonance Imaging 50, 410–416 (2019).
doi: 10.1002/jmri.26612
pubmed: 30637852
pmcid: 6766866
Ogg, R. J. & Steen, R. G. Age-related changes in brain t1 are correlated with iron concentration. Magnetic resonance in medicine 40, 749–753 (1998).
doi: 10.1002/mrm.1910400516
pubmed: 9797159
Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and t1-mapping at high field. Neuroimage 49, 1271–1281 (2010).
doi: 10.1016/j.neuroimage.2009.10.002
pubmed: 19819338
Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. Elastix: A toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010).
doi: 10.1109/TMI.2009.2035616
pubmed: 19923044
Morel, B. et al. Normal volumetric and t1 relaxation time values at 1.5t in segmented pediatric brain mri using a mp2rage acquisition. Eur. Radiol. 31, 1505–1516 (2021).
doi: 10.1007/s00330-020-07194-w
pubmed: 32885296
Schmitter, D. et al. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and alzheimer’s disease. NeuroImage Clin. 7, 7–17 (2015).
doi: 10.1016/j.nicl.2014.11.001
pubmed: 25429357
Fujimoto, K. et al. Quantitative comparison of cortical surface reconstructions from MP2RAGE and multi-echo MPRAGE data at 3 and 7 T. Neuroimage 90, 60–73 (2014).
doi: 10.1016/j.neuroimage.2013.12.012
pubmed: 24345388
Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, https://doi.org/10.1038/sdata.2016.44 (2016).
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980, https://doi.org/10.1016/j.neuroimage.2006.01.021 (2006).
doi: 10.1016/j.neuroimage.2006.01.021
pubmed: 16530430
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. FSL. NeuroImage 62, 782–790, https://doi.org/10.1016/j.neuroimage.2011.09.015 (2012).
doi: 10.1016/j.neuroimage.2011.09.015
pubmed: 21979382
Frackowiak, R. et al (eds.) Human Brain Function (Academic Press USA, 1997).
VideoLan. Vlc media player (2006).
AG Teixeira, R. P. et al. Controlled saturation magnetization transfer for reproducible multivendor variable flip angle t1 and t2 mapping. Magnetic Resonance in Medicine 84, 221–236 (2020).
doi: 10.1002/mrm.28109
Bojorquez, J. Z. et al. What are normal relaxation times of tissues at 3 t? Magnetic resonance imaging 35, 69–80 (2017).
doi: 10.1016/j.mri.2016.08.021
pubmed: 27594531