A global multicohort study to map subcortical brain development and cognition in infancy and early childhood.
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
23 Nov 2023
23 Nov 2023
Historique:
received:
11
04
2022
accepted:
16
10
2023
medline:
24
11
2023
pubmed:
24
11
2023
entrez:
23
11
2023
Statut:
aheadofprint
Résumé
The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.
Identifiants
pubmed: 37996530
doi: 10.1038/s41593-023-01501-6
pii: 10.1038/s41593-023-01501-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIBIB NIH HHS
ID : U54 EB020403
Pays : United States
Organisme : NIAAA NIH HHS
ID : U24 AA014811
Pays : United States
Organisme : NIBIB NIH HHS
ID : U54 EB020403
Pays : United States
Organisme : NIBIB NIH HHS
ID : U54 EB020403
Pays : United States
Organisme : Academy of Medical Sciences
ID : NAF002/1001
Pays : United Kingdom
Informations de copyright
© 2023. The Author(s).
Références
Stiles, J. & Jernigan, T. L. The basics of brain development. Neuropsychol. Rev. 20, 327–348 (2010).
Feldman, R. & Eidelman, A. I. Biological and environmental initial conditions shape the trajectories of cognitive and social–emotional development across the first years of life. Dev. Sci. 12, 194–200 (2009).
pubmed: 19120428
doi: 10.1111/j.1467-7687.2008.00761.x
Gao, W. et al. A review on neuroimaging studies of genetic and environmental influences on early brain development. NeuroImage 185, 802–812 (2019).
Gilmore, J. H., Knickmeyer, R. C. & Gao, W. Imaging structural and functional brain development in early childhood. Nat. Rev. Neurosci. 19, 123–137 (2018).
pubmed: 29449712
pmcid: 5987539
doi: 10.1038/nrn.2018.1
Vijayakumar, N., Mills, K. L., Alexander-Bloch, A., Tamnes, C. K. & Whittle, S. Structural brain development: a review of methodological approaches and best practices. Dev. Cogn. Neurosci. 33, 129–148 (2018).
pubmed: 29221915
doi: 10.1016/j.dcn.2017.11.008
Kraemer, H. C., Yesavage, J. A., Taylor, J. L. & Kupfer, D. How can we learn about developmental processes from cross-sectional studies, or can we? Am. J. Psychiatry 157, 163–171 (2000).
pubmed: 10671382
doi: 10.1176/appi.ajp.157.2.163
Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).
pubmed: 35296861
pmcid: 8991999
doi: 10.1038/s41586-022-04492-9
Klapwijk, E. T., van den Bos, W., Tamnes, C. K., Raschle, N. M. & Mills, K. L. Opportunities for increased reproducibility and replicability of developmental neuroimaging. Dev. Cogn. Neurosci. 47, 100902 (2021).
Schmaal, L., Ching, C. R. K., McMahon, A. B., Jahanshad, N. & Thompson, P. M. in Personalized Psychiatry (ed Baune, B. T.) 483–497 (Elsevier, 2019).
Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).
pubmed: 35388223
pmcid: 9021021
doi: 10.1038/s41586-022-04554-y
Olson, L., Chen, B. & Fishman, I. Neural correlates of socioeconomic status in early childhood: a systematic review of the literature. Child Neuropsychol. 27, 390–423 (2021).
Kaczkurkin, A. N., Raznahan, A. & Satterthwaite, T. D. Sex differences in the developing brain: insights from multimodal neuroimaging. Neuropsychopharmacology 44, 71–85 (2019).
pubmed: 29930385
doi: 10.1038/s41386-018-0111-z
Gurvich, C., Thomas, N. & Kulkarni, J. Sex differences in cognition and aging and the influence of sex hormones. Handb. Clin. Neurol. 175, 103–115 (2020).
pubmed: 33008519
doi: 10.1016/B978-0-444-64123-6.00008-4
Lean, R. E., Neil, J. J. & Smyser, C. D. in Handbook of Pediatric Brain Imaging Vol. 2 (eds Huang, H. & Roberts, T. P. L.) 429–465 (Academic Press, 2021).
Farah, M. J. The neuroscience of socioeconomic status: correlates, causes, and consequences. Neuron 96, 56–71 (2017).
Shonkoff, J. P., Boyce, W. T. & McEwen, B. S. Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. JAMA 301, 2252–2259 (2009).
Koziol, L. F., Barker, L. A., Joyce, A. W. & Hrin, S. The small-world organization of large-scale brain systems and relationships with subcortical structures. 3, 245–252 (2014).
Pietschnig, J., Penke, L., Wicherts, J. M., Zeiler, M. & Voracek, M. Meta-analysis of associations between human brain volume and intelligence differences: how strong are they and what do they mean? Neurosci. Biobehav. Rev. 57, 411–432 (2015).
Girault, J. B. et al. The predictive value of developmental assessments at 1 and 2 for intelligence quotients at 6. Intelligence 68, 58–65 (2018).
pubmed: 30270948
pmcid: 6157738
doi: 10.1016/j.intell.2018.03.003
Thompson, P. M. et al. ENIGMA and global neuroscience: a decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl. Psychiatry 10, 100 (2020).
pubmed: 32198361
pmcid: 7083923
doi: 10.1038/s41398-020-0705-1
Whitwell, J. L., Crum, W. R., Watt, H. C. & Fox, N. C. Normalization of cerebral volumes by use of intracranial volume: implications for longitudinal quantitative MR imaging. AJNR Am. J. Neuroradiol. 22, 1483–1489 (2001).
pubmed: 11559495
pmcid: 7974589
Tottenham, N. & Gabard-Durnam, L. J. The developing amygdala: a student of the world and a teacher of the cortex. Curr. Opin. Psychol. 17, 55–60 (2017).
Lee, J. K., Johnson, E. G. & Ghetti, S. in The Hippocampus from Cells to Systems: Structure, Connectivity, and Functional Contributions to Memory and Flexible Cognition (eds Hannula, D. E. & Duff, M. C.) 141–166 (Springer International Publishing, 2017).
Sutton, J. E., Joanisse, M. F. & Newcombe, N. S. Spinning in the scanner: neural correlates of virtual reorientation. J. Exp. Psychol. Learn Mem. Cogn. 36, 1097–1107 (2010).
pubmed: 20804287
doi: 10.1037/a0019938
Wierenga, L. et al. Typical development of basal ganglia, hippocampus, amygdala and cerebellum from age 7 to 24. NeuroImage 96, 67–72 (2014).
pubmed: 24705201
doi: 10.1016/j.neuroimage.2014.03.072
Lenroot, R. K. et al. Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage 36, 1065–1073 (2007).
pubmed: 17513132
doi: 10.1016/j.neuroimage.2007.03.053
Tutunji, R. et al. Thalamic volume and dimensions on MRI in the pediatric population: normative values and correlations: (a cross sectional study). Eur. J. Radiol. 109, 27–32 (2018).
pubmed: 30527308
doi: 10.1016/j.ejrad.2018.10.018
Panzica, G. C. & Melcangi, R. C. Structural and molecular brain sexual differences: a tool to understand sex differences in health and disease. Neurosci. Biobehav. Rev. 67, 2–8 (2016).
pubmed: 27113294
doi: 10.1016/j.neubiorev.2016.04.017
Chawanpaiboon, S. et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob. Health 7, e37–e46 (2019).
pubmed: 30389451
doi: 10.1016/S2214-109X(18)30451-0
Duncan, G. J. & Magnuson, K. Socioeconomic status and cognitive functioning: moving from correlation to causation. Wiley Interdiscip. Rev. Cogn. Sci. 3, 377–386 (2012).
pubmed: 26301469
doi: 10.1002/wcs.1176
Augustine, J. M., Cavanagh, S. E. & Crosnoe, R. Maternal education, early child care and the reproduction of advantage. Soc. Forces 88, 1–29 (2009).
pubmed: 20671797
pmcid: 2910916
doi: 10.1353/sof.0.0233
Girault, J. B. et al. White matter microstructural development and cognitive ability in the first 2 years of life. Hum. Brain Mapp. 40, 1195–1210 (2019).
pubmed: 30353962
doi: 10.1002/hbm.24439
Dai, X., Hadjipantelis, P., Wang, J., Deoni, S. C. L. & Müller, H. Longitudinal associations between white matter maturation and cognitive development across early childhood. Hum. Brain Mapp. 40, 4130–4145 (2019).
pubmed: 31187920
pmcid: 6771612
doi: 10.1002/hbm.24690
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
pubmed: 27225129
pmcid: 4883595
doi: 10.1038/nature17671
Adams, H. H. H. et al. Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat. Neurosci. 19, 1569–1582 (2016).
pubmed: 27694991
pmcid: 5227112
doi: 10.1038/nn.4398
Evans, G. W. et al. Childhood cumulative risk exposure and adult amygdala volume and function. J. Neurosci. Res. 94, 535–543 (2016).
pubmed: 26469872
doi: 10.1002/jnr.23681
Graham, A. M., Pfeifer, J. H., Fisher, P. A., Carpenter, S. & Fair, D. A. Early life stress is associated with default system integrity and emotionality during infancy. J. Child Psychol. Psychiatry 56, 1212–1222 (2015).
pubmed: 25809052
pmcid: 4580514
doi: 10.1111/jcpp.12409
Turesky, T. K. et al. The relationship between biological and psychosocial risk factors and resting-state functional connectivity in 2-month-old Bangladeshi infants: a feasibility and pilot study. Dev. Sci. 22, e12841 (2019).
pubmed: 31016808
pmcid: 6713583
doi: 10.1111/desc.12841
Noble, K. G. & Giebler, M. A. The neuroscience of socioeconomic inequality. Curr. Opin. Behav. Sci. 36, 23–28 (2020).
pubmed: 32719820
pmcid: 7384696
doi: 10.1016/j.cobeha.2020.05.007
Assari, S., Boyce, S. & Bazargan, M. Subjective socioeconomic status and children’s amygdala volume: minorities’ diminish returns. NeuroSci 1, 59–74 (2020).
pubmed: 33103157
doi: 10.3390/neurosci1020006
Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773–778 (2015).
pubmed: 25821911
pmcid: 4414816
doi: 10.1038/nn.3983
Ellwood-Lowe, M. E. et al. Time-varying effects of income on hippocampal volume trajectories in adolescent girls. Dev. Cogn. Neurosci. 30, 41–50 (2018).
pubmed: 29275097
doi: 10.1016/j.dcn.2017.12.005
McDermott, C. L. et al. Longitudinally mapping childhood socioeconomic status associations with cortical and subcortical morphology. J. Neurosci. 39, 1365–1373 (2019).
pubmed: 30587541
pmcid: 6381251
doi: 10.1523/JNEUROSCI.1808-18.2018
Herrero, M. T., Barcia, C. & Navarro, J. M. Functional anatomy of thalamus and basal ganglia. Childs Nerv. Syst. 18, 386–404 (2002).
Hanson, J. L., Chandra, A., Wolfe, B. L. & Pollak, S. D. Association between income and the hippocampus. PLoS ONE 6, e18712 (2011).
pubmed: 21573231
pmcid: 3087752
doi: 10.1371/journal.pone.0018712
Zarif, H., Nicolas, S., Petit-Paitel, A., Chabry, J. & Guyon, Alice. in The Hippocampus—Plasticity and Functions (ed Stuchlik A.) Ch. 1 (IntechOpen, 2017).
Jenkins, L. M. et al. Subcortical structural variations associated with low socioeconomic status in adolescents. Hum. Brain Mapp. 41, 162–171 (2020).
pubmed: 31571360
doi: 10.1002/hbm.24796
Leonard, J. A., Mackey, A. P., Finn, A. S. & Gabrieli, J. D. E. Differential effects of socioeconomic status on working and procedural memory systems. Front. Hum. Neurosci. 9, 554 (2015).
pubmed: 26500525
pmcid: 4597101
doi: 10.3389/fnhum.2015.00554
Fareri, D. S. & Tottenham, N. Effects of early life stress on amygdala and striatal development. Dev. Cogn. Neurosci. 19, 233–247 (2016).
pubmed: 27174149
pmcid: 4912892
doi: 10.1016/j.dcn.2016.04.005
Knickmeyer, R. C. et al. Impact of demographic and obstetric factors on infant brain volumes: a population neuroscience study. Cereb. Cortex 27, 5616–5625 (2016).
pmcid: 6075568
Mullen, E. Mullen Scales of Early Learning (AGS Publishing, 1995).
Seger, C. A. How do the basal ganglia contribute to categorization? Their role in generalization, response selection, and learning via feedback. Neurosci. Biobehav. Rev. 32, 265–278 (2008).
pubmed: 17919725
doi: 10.1016/j.neubiorev.2007.07.010
Lisman, J. E. & Otmakhova, N. A. Storage, recall, and novelty detection of sequences by the hippocampus: elaborating on the SOCRATIC model to account for normal and aberrant effects of dopamine. Hippocampus 11, 551–568 (2001).
pubmed: 11732708
doi: 10.1002/hipo.1071
Groeschel, S., Vollmer, B., King, M. D. & Connelly, A. Developmental changes in cerebral grey and white matter volume from infancy to adulthood. Int. J. Dev. Neurosci. 28, 481–489 (2010).
pubmed: 20600789
doi: 10.1016/j.ijdevneu.2010.06.004
Shen, M. D. Cerebrospinal fluid and the early brain development of autism. J. Neurodev. Disord. 10, 39 (2018).
pubmed: 30541429
pmcid: 6292033
doi: 10.1186/s11689-018-9256-7
Buyanova, I. S. & Arsalidou, M. Cerebral white matter myelination and relations to age, gender, and cognition: a selective review. Front. Hum. Neurosci. 15, 662031 (2021).
pubmed: 34295229
pmcid: 8290169
doi: 10.3389/fnhum.2021.662031
Turesky, T. K. et al. Brain morphometry and diminished physical growth in Bangladeshi children growing up in extreme poverty: a longitudinal study. Dev. Cogn. Neurosci. 52, 101029 (2021).
pubmed: 34801857
pmcid: 8605388
doi: 10.1016/j.dcn.2021.101029
OECD. Education at a glance 2019: OECD indicators. https://doi.org/10.1787/f8d7880d-en (OECD Publishing, 2019).
Zhu, J. et al. Integrated structural and functional atlases of Asian children from infancy to childhood. NeuroImage 245, 118716 (2021).
pubmed: 34767941
doi: 10.1016/j.neuroimage.2021.118716
Du, J., Younes, L. & Qiu, A. Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images. NeuroImage 56, 162–173 (2011).
pubmed: 21281722
doi: 10.1016/j.neuroimage.2011.01.067
Poh, J. S. et al. Developmental synchrony of thalamocortical circuits in the neonatal brain. NeuroImage 116, 168–176 (2015).
pubmed: 25812713
doi: 10.1016/j.neuroimage.2015.03.039
Qiu, A. et al. COMT haplotypes modulate associations of antenatal maternal anxiety and neonatal cortical morphology. Am. J. Psychiatry 172, 163–172 (2015).
pubmed: 25320962
doi: 10.1176/appi.ajp.2014.14030313
Qiu, A. et al. Maternal anxiety and infants’ hippocampal development: timing matters. Transl. Psychiatry 3, e306 (2013).
pubmed: 24064710
pmcid: 3784768
doi: 10.1038/tp.2013.79
Ducharme, S. et al. Trajectories of cortical thickness maturation in normal brain development—the importance of quality control procedures for the Brain Development Cooperative Group HHS Public Access. NeuroImage 125, 267–279 (2016).
pubmed: 26463175
doi: 10.1016/j.neuroimage.2015.10.010
Shi, F. et al. Infant brain atlases from neonates to 1- and 2-year-olds. PLoS ONE 6, e18746 (2011).
pubmed: 21533194
pmcid: 3077403
doi: 10.1371/journal.pone.0018746
Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273–289 (2002).
pubmed: 11771995
doi: 10.1006/nimg.2001.0978
Wang, J. et al. Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline. Front. Neuroinform. 8, 7 (2014).
pubmed: 24567717
pmcid: 3915103
doi: 10.3389/fninf.2014.00007
Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31, 1116–1128 (2006).
pubmed: 16545965
doi: 10.1016/j.neuroimage.2006.01.015
Maltbie, E. et al. Asymmetric bias in user guided segmentations of brain structures. NeuroImage 59, 1315–1323 (2012).
pubmed: 21889995
doi: 10.1016/j.neuroimage.2011.08.025
Gousias, I. S. et al. Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions. PLoS ONE 8, e59990 (2013).
pubmed: 23565180
pmcid: 3615077
doi: 10.1371/journal.pone.0059990
Zöllei, L., Iglesias, J. E., Ou, Y., Grant, P. E. & Fischl, B. Infant FreeSurfer: an automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0–2 years. NeuroImage 218, 116946 (2020).
pubmed: 32442637
doi: 10.1016/j.neuroimage.2020.116946
Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).
pubmed: 22248573
doi: 10.1016/j.neuroimage.2012.01.021
Ghosh, S. S. et al. Evaluating the validity of volume-based and surface-based brain image registration for developmental cognitive neuroscience studies in children 4 to 11 years of age. NeuroImage 53, 85–93 (2010).
pubmed: 20621657
doi: 10.1016/j.neuroimage.2010.05.075
Puonti, O., Iglesias, J. E. & Van Leemput, K. Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. NeuroImage 143, 235–249 (2016).
pubmed: 27612647
doi: 10.1016/j.neuroimage.2016.09.011
Naigles, L. R. et al. Neural correlates of language variability in preschool-aged boys with autism spectrum disorder. Autism Res. 10, 1107–1119 (2017).
pubmed: 28301102
pmcid: 5548458
doi: 10.1002/aur.1756
Padmapriya, N. et al. Association of physical activity and sedentary behavior with depression and anxiety symptoms during pregnancy in a multiethnic cohort of Asian women. Arch. Women’s Ment. Health 19, 1119–1128 (2016).
doi: 10.1007/s00737-016-0664-y
Quah, P. L. et al. Validation of the Children’s Eating Behavior Questionnaire in 3 year old children of a multi-ethnic Asian population: the GUSTO cohort study. Appetite 113, 100–105 (2017).
pubmed: 28232104
pmcid: 5384631
doi: 10.1016/j.appet.2017.02.024
Donald, K. A. et al. Risk and protective factors for child development: an observational South African birth cohort. PLoS Med. 16, e1002920 (2019).
pubmed: 31560687
pmcid: 6764658
doi: 10.1371/journal.pmed.1002920
U. S. Department of Health and Human Services. 2021 Poverty Guidelines. https://aspe.hhs.gov/2021-poverty-guidelines (2021).
Davidian, M. & Giltinan, D. M. Nonlinear models for repeated measurement data: an overview and update. J. Agric. Biol. Environ. Stat. 8, 387–419 (2003).
doi: 10.1198/1085711032697
Voevodskaya, O. et al. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer’s disease. Front. Aging Neurosci. 6, 264 (2014).
pubmed: 25339897
pmcid: 4188138
doi: 10.3389/fnagi.2014.00264
Dhamala, E. et al. Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development. NeuroImage 260, 119485 (2022).
pubmed: 35843514
doi: 10.1016/j.neuroimage.2022.119485
Sanchis-Segura, C., Ibañez-Gual, M. V., Aguirre, N., Gómez-Cruz, Á. J. & Forn, C. Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction. Sci. Rep. 10, 12953 (2020).
pubmed: 32737332
pmcid: 7395772
doi: 10.1038/s41598-020-69361-9
Sanchis-Segura, C. et al. Sex differences in gray matter volume: how many and how large are they really? Biol. Sex Differ. 10, 32 (2019).
pubmed: 31262342
pmcid: 6604149
doi: 10.1186/s13293-019-0245-7
Caspi, Y. et al. Changes in the intracranial volume from early adulthood to the sixth decade of life: a longitudinal study. NeuroImage 220, 116842 (2020).
pubmed: 32339774
doi: 10.1016/j.neuroimage.2020.116842
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
McCall, R. B., Hogarty, P. S. & Hurlburt, N. Transitions in infant sensorimotor development and the prediction of childhood IQ. Am. Psychol. 27, 728–748 (1972).
Rousselet, G., Pernet, D. C. & Wilcox, R. R. An introduction to the bootstrap: a versatile method to make inferences by using data-driven simulations. Meta-Psychol. (in the press).
Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. mediation: R package for causal mediation analysis. J. Stat. Softw. 59, 1–38 (2014).
doi: 10.18637/jss.v059.i05