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Imaging structural and functional brain development in early childhood

Nature Reviews Neuroscience volume 19, pages 123–137 (2018)Cite this article

Subjects

Key Points

  • Adult patterns of regional cortical thickness seem to be present at birth, and cortical grey matter expands rapidly in the first year of life. Cortical thickness peaks between 1 and 2 years, whereas surface area expands through childhood

  • White-matter tracts are established before birth, and postnatal myelination of these tracts occurs rapidly over the first 2 years of life. The white-matter connectome is fairly mature at birth

  • Sensorimotor resting-state functional networks are present at birth, whereas higher-order functional networks gradually emerge and develop over the first 2 years of life

  • Studies have begun to explore genetic and environmental influences on early-childhood brain development and the predictive value of early imaging biomarkers

  • Future studies will need to better define normal and abnormal brain development in early childhood and determine whether it is possible to identify early imaging biomarkers of later cognitive and behavioural outcomes

Abstract

In humans, the period from term birth to ∼2 years of age is characterized by rapid and dynamic brain development and plays an important role in cognitive development and risk of disorders such as autism and schizophrenia. Recent imaging studies have begun to delineate the growth trajectories of brain structure and function in the first years after birth and their relationship to cognition and risk of neuropsychiatric disorders. This Review discusses the development of grey and white matter and structural and functional networks, as well as genetic and environmental influences on early-childhood brain development. We also discuss initial evidence regarding the usefulness of early imaging biomarkers for predicting cognitive outcomes and risk of neuropsychiatric disorders.

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Figure 1: Structural brain development in early childhood.
Figure 2: Estimated trajectories of brain structural parameters during development.
Figure 3: The development of resting-state functional networks during infancy.

References

  1. Nelson, C. A. et al. Cognitive recovery in socially deprived young children: the Bucharest Early Intervention Project. Science 318, 1937–1940 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. National Advisory Mental Health Council's Workgroup.Transformative Neurodevelopmental Research in Mental Illness (US National Institute of Mental Health, 2008).

  3. Knickmeyer, R. C. et al. A structural MRI study of human brain development from birth to 2 years. J. Neurosci. 28, 12176–12182 (2008). This is a comprehensive study of structural brain development in the first 2 years of life demonstrating the rapid growth of cortical grey matter in the first year of life.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lyall, A. E. et al. Dynamic development of regional cortical thickness and surface area in early childhood. Cereb. Cortex 25, 2204–2212 (2015).

    Article  PubMed  Google Scholar 

  5. Dubois, J. et al. The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience 276, 48–71 (2014). This is a comprehensive review of the use of imaging to study white matter in early brain development.

    Article  CAS  PubMed  Google Scholar 

  6. Gao, W. et al. Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc. Natl Acad. Sci. USA 106, 6790–6795 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Petanjek, Z. et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc. Natl Acad. Sci. USA 108, 13281–13286 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Pletikos, M. et al. Temporal specification and bilaterality of human neocortical topographic gene expression. Neuron 81, 321–332 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Geng, X. et al. Structural and maturational covariance in early childhood brain development. Cereb. Cortex 27, 1795–1807 (2017).

    PubMed  Google Scholar 

  10. Gilmore, J. H. et al. Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb. Cortex 22, 2478–2485 (2012).

    Article  PubMed  Google Scholar 

  11. Anderson, P. J., Cheong, J. L. & Thompson, D. K. The predictive validity of neonatal MRI for neurodevelopmental outcome in very preterm children. Semin. Perinatol. 39, 147–158 (2015).

    Article  PubMed  Google Scholar 

  12. Jakab, A. et al. Fetal cerebral magnetic resonance imaging beyond morphology. Semin. Ultrasound CT MR 36, 465–475 (2015).

    Article  PubMed  Google Scholar 

  13. Gilmore, J. H. et al. Regional gray matter growth, sexual dimorphism, and cerebral asymmetry in the neonatal brain. J. Neurosci. 27, 1255–1260 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Courchesne, E. et al. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 216, 672–682 (2000).

    Article  CAS  PubMed  Google Scholar 

  15. Matsuzawa, J. et al. Age-related volumetric changes of brain gray and white matter in healthy infants and children. Cereb. Cortex 11, 335–342 (2001).

    Article  CAS  PubMed  Google Scholar 

  16. Holland, D. et al. Structural growth trajectories and rates of change in the first 3 months of infant brain development. JAMA Neurol. 71, 1266–1274 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  17. 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).

    Article  CAS  PubMed  Google Scholar 

  18. Mills, K. L. et al. Structural brain development between childhood and adulthood: convergence across four longitudinal samples. Neuroimage 141, 273–281 (2016).

    Article  PubMed  Google Scholar 

  19. Bompard, L. et al. Multivariate longitudinal shape analysis of human lateral ventricles during the first twenty-four months of life. PLoS ONE 9, e108306 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Raznahan, A. et al. How does your cortex grow? J. Neurosci. 31, 7174–7177 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Shaw, P. et al. Neurodevelopmental trajectories of the human cerebral cortex. J. Neurosci. 28, 3586–3594 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Remer, J. et al. Quantifying cortical development in typically developing toddlers and young children, 1–6 years of age. Neuroimage 153, 246–261 (2017).

    Article  PubMed  Google Scholar 

  23. Li, G. et al. Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. Cereb. Cortex 23, 2724–2733 (2013).

    Article  PubMed  Google Scholar 

  24. Hill, J. et al. Similar patterns of cortical expansion during human development and evolution. Proc. Natl Acad. Sci. USA 107, 13135–13140 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Brown, T. T. et al. Neuroanatomical assessment of biological maturity. Curr. Biol. 22, 1693–1698 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Li, G., Lin, W., Gilmore, J. H. & Shen, D. Spatial patterns, longitudinal development, and hemispheric asymmetries of cortical thickness in infants from birth to 2 years of age. J. Neurosci. 35, 9150–9162 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ducharme, S. et al. Trajectories of cortical thickness maturation in normal brain development — the importance of quality control procedures. Neuroimage 125, 267–279 (2016).

    Article  PubMed  Google Scholar 

  28. Walhovd, K. B., Fjell, A. M., Giedd, J., Dale, A. M. & Brown, T. T. Through thick and thin: a need to reconcile contradictory results on trajectories in human cortical development. Cereb. Cortex 27, 1472–1481 (2017).

    PubMed  Google Scholar 

  29. Schoenemann, P. T., Sheehan, M. J. & Glotzer, L. D. Prefrontal white matter volume is disproportionately larger in humans than in other primates. Nat. Neurosci. 8, 242–252 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Zhang, K. & Sejnowski, T. J. A universal scaling law between gray matter and white matter of cerebral cortex. Proc. Natl Acad. Sci. USA 97, 5621–5626 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Qiu, A., Mori, S. & Miller, M. I. Diffusion tensor imaging for understanding brain development in early life. Annu. Rev. Psychol. 66, 853–876 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Geng, X. et al. Quantitative tract-based white matter development from birth to age 2 years. Neuroimage 61, 542–557 (2012).

    Article  PubMed  Google Scholar 

  33. Deoni, S. C. et al. Mapping infant brain myelination with magnetic resonance imaging. J. Neurosci. 31, 784–791 (2011). This paper presents the initial imaging study of myelin development in the human infant using the myelin water fraction approach, a more direct assessment of myelin than standard diffusion-weighted imaging.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Faria, A. V. et al. Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection. Neuroimage 52, 415–428 (2010).

    Article  PubMed  Google Scholar 

  35. Krogsrud, S. K. et al. Changes in white matter microstructure in the developing brain — a longitudinal diffusion tensor imaging study of children from 4 to 11 years of age. Neuroimage 124, 473–486 (2016).

    Article  PubMed  Google Scholar 

  36. Dean, D. C. et al. Modeling healthy male white matter and myelin development: 3 through 60 months of age. Neuroimage 84, 742–752 (2014).

    Article  PubMed  Google Scholar 

  37. Dean, D. C. et al. Characterizing longitudinal white matter development during early childhood. Brain Struct. Funct. 220, 1921–1933 (2015).

    Article  PubMed  Google Scholar 

  38. Lee, S. J. et al. Common and heritable components of white matter microstructure predict cognitive function at 1 and 2 y. Proc. Natl Acad. Sci. USA 114, 148–153 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).

    Article  CAS  PubMed  Google Scholar 

  40. Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).

    Article  PubMed  Google Scholar 

  41. Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336–349 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. Khundrakpam, B. S., Lewis, J. D., Zhao, L., Chouinard-Decorte, F. & Evans, A. C. Brain connectivity in normally developing children and adolescents. Neuroimage 134, 192–203 (2016).

    Article  PubMed  Google Scholar 

  43. Richmond, S., Johnson, K. A., Seal, M. L., Allen, N. B. & Whittle, S. Development of brain networks and relevance of environmental and genetic factors: a systematic review. Neurosci. Biobehav. Rev. 71, 215–239 (2016).

    Article  PubMed  Google Scholar 

  44. Cao, M., Huang, H. & He, Y. Developmental connectomics from infancy through early childhood. Trends Neurosci. 40, 494–506 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Senden, M., Reuter, N., van den Heuvel, M. P., Goebel, R. & Deco, G. Cortical rich club regions can organize state-dependent functional network formation by engaging in oscillatory behavior. Neuroimage 146, 561–574 (2017).

    Article  PubMed  Google Scholar 

  46. Yan, G. et al. Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature 550, 519–523 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Cao, M., Huang, H., Peng, Y., Dong, Q. & He, Y. Toward developmental connectomics of the human brain. Front. Neuroanat. 10, 25 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Yap, P. T. et al. Development trends of white matter connectivity in the first years of life. PLoS ONE 6, e24678 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. van den Heuvel, M. P. et al. The neonatal connectome during preterm brain development. Cereb. Cortex 25, 3000–3013 (2015).

    Article  PubMed  Google Scholar 

  50. Ball, G. et al. Rich-club organization of the newborn human brain. Proc. Natl Acad. Sci. USA 111, 7456–7461 (2014). This paper presents an early study of the white-matter connectome in preterm and term infants, demonstrating that major hubs are already present at 30 weeks gestational age.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Huang, H. et al. Development of human brain structural networks through infancy and childhood. Cereb. Cortex 25, 1389–1404 (2015).

    Article  PubMed  Google Scholar 

  52. Hagmann, P. et al. White matter maturation reshapes structural connectivity in the late developing human brain. Proc. Natl Acad. Sci. USA 107, 19067–19072 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Evans, A. C. Networks of anatomical covariance. Neuroimage 80, 489–504 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Alexander-Bloch, A., Raznahan, A., Bullmore, E. & Giedd, J. The convergence of maturational change and structural covariance in human cortical networks. J. Neurosci. 33, 2889–2899 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Schmitt, J. E. et al. Identification of genetically mediated cortical networks: a multivariate study of pediatric twins and siblings. Cereb. Cortex 18, 1737–1747 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Alexander-Bloch, A., Giedd, J. N. & Bullmore, E. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322–336 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Khundrakpam, B. S. et al. Imaging structural covariance in the development of intelligence. Neuroimage 144, 227–240 (2017).

    Article  PubMed  Google Scholar 

  58. Fan, Y. et al. Brain anatomical networks in early human brain development. Neuroimage 54, 1862–1871 (2011).

    Article  PubMed  Google Scholar 

  59. Nie, J., Li, G. & Shen, D. Development of cortical anatomical properties from early childhood to early adulthood. Neuroimage 76, 216–224 (2013).

    Article  PubMed  Google Scholar 

  60. Zielinski, B. A., Gennatas, E. D., Zhou, J. & Seeley, W. W. Network-level structural covariance in the developing brain. Proc. Natl Acad. Sci. USA 107, 18191–18196 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Khundrakpam, B. S. et al. Developmental changes in organization of structural brain networks. Cereb. Cortex 23, 2072–2085 (2013).

    Article  PubMed  Google Scholar 

  62. Khazipov, R. & Luhmann, H. J. Early patterns of electrical activity in the developing cerebral cortex of humans and rodents. Trends Neurosci. 29, 414–418 (2006).

    Article  CAS  PubMed  Google Scholar 

  63. Dehaene-Lambertz, G. & Spelke, E. S. The infancy of the human brain. Neuron 88, 93–109 (2015).

    Article  CAS  PubMed  Google Scholar 

  64. Dreyfus-Brisac, C. & Larroche, J. C. Discontinuous electroencephalograms in the premature newborn and at term. Electro-anatomo-clinical correlations [French]. Rev. Electroencephalogr. Neurophysiol. Clin. 1, 95–99 (1971).

    Article  CAS  PubMed  Google Scholar 

  65. Anderson, C. M., Torres, F. & Faoro, A. The EEG of the early premature. Electroencephalogr. Clin. Neurophysiol. 60, 95–105 (1985).

    Article  CAS  PubMed  Google Scholar 

  66. Arichi, T. et al. Localization of spontaneous bursting neuronal activity in the preterm human brain with simultaneous EEG-fMRI. eLife 6, e27814 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Hanganu, I. L., Ben-Ari, Y. & Khazipov, R. Retinal waves trigger spindle bursts in the neonatal rat visual cortex. J. Neurosci. 26, 6728–6736 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ackman, J. B., Burbridge, T. J. & Crair, M. C. Retinal waves coordinate patterned activity throughout the developing visual system. Nature 490, 219–225 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Tritsch, N. X., Yi, E., Gale, J. E., Glowatzki, E. & Bergles, D. E. The origin of spontaneous activity in the developing auditory system. Nature 450, 50–55 (2007).

    Article  CAS  PubMed  Google Scholar 

  70. Blumberg, M. S. Developing sensorimotor systems in our sleep. Curr. Direct. Psychol. Sci. 24, 32–37 (2015).

    Article  Google Scholar 

  71. Kwong, K. K. et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl Acad. Sci. USA 89, 5675–5679 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Ogawa, S. et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl Acad. Sci. USA 89, 5951–5955 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Jobsis, F. F. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198, 1264–1267 (1977).

    Article  CAS  PubMed  Google Scholar 

  74. Arichi, T. et al. Development of BOLD signal hemodynamic responses in the human brain. Neuroimage 63, 663–673 (2012).

    Article  PubMed  Google Scholar 

  75. Allievi, A. G. et al. Maturation of sensori-motor functional responses in the preterm brain. Cereb. Cortex 26, 402–413 (2016).

    Article  PubMed  Google Scholar 

  76. Karen, T. et al. Hemodynamic response to visual stimulation in newborn infants using functional near-infrared spectroscopy. Hum. Brain Mapp. 29, 453–460 (2008).

    Article  PubMed  Google Scholar 

  77. Dehaene-Lambertz, G., Dehaene, S. & Hertz-Pannier, L. Functional neuroimaging of speech perception in infants. Science 298, 2013–2015 (2002). This is an important early task-based functional imaging study that demonstrated that adult language areas are already active in 2-month-olds to 3 month-olds.

    Article  CAS  PubMed  Google Scholar 

  78. Wilcox, T., Haslup, J. A. & Boas, D. A. Dissociation of processing of featural and spatiotemporal information in the infant cortex. Neuroimage 53, 1256–1263 (2010).

    Article  PubMed  Google Scholar 

  79. Wilcox, T., Stubbs, J., Hirshkowitz, A. & Boas, D. Object processing and functional organization of the infant cortex. Neuroimage 62, 1833–1840 (2012).

    Article  PubMed  Google Scholar 

  80. Nakano, T., Watanabe, H., Homae, F. & Taga, G. Prefrontal cortical involvement in young infants' analysis of novelty. Cereb. Cortex 19, 455–463 (2009).

    Article  PubMed  Google Scholar 

  81. Grossmann, T. & Johnson, M. H. Selective prefrontal cortex responses to joint attention in early infancy. Biol. Lett. 6, 540–543 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Kozberg, M. & Hillman, E. Neurovascular coupling and energy metabolism in the developing brain. Prog. Brain Res. 225, 213–242 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995).

    Article  CAS  PubMed  Google Scholar 

  84. Gao, W., Lin, W., Grewen, K. & Gilmore, J. H. Functional connectivity of the infant human brain: plastic and modifiable. Neuroscientist 23, 169–184 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Fransson, P. et al. Resting-state networks in the infant brain. Proc. Natl Acad. Sci. USA 104, 15531–15536 (2007). This study delineates resting-state functional networks in human infants through the use of a cohort of sedated premature infants.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Lin, W. et al. Functional connectivity MR imaging reveals cortical functional connectivity in the developing brain. AJNR Am. J. Neuroradiol. 29, 1883–1889 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Liu, W. C., Flax, J. F., Guise, K. G., Sukul, V. & Benasich, A. A. Functional connectivity of the sensorimotor area in naturally sleeping infants. Brain Res. 1223, 42–49 (2008).

    Article  CAS  PubMed  Google Scholar 

  88. Smyser, C. D. et al. Longitudinal analysis of neural network development in preterm infants. Cereb. Cortex 20, 2852–2862 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Gao, W., Alcauter, S., Smith, J. K., Gilmore, J. H. & Lin, W. Development of human brain cortical network architecture during infancy. Brain Struct. Funct. 220, 1173–1186 (2015).

    Article  PubMed  Google Scholar 

  90. Gao, W. et al. Functional network development during the first year: relative sequence and socioeconomic correlations. Cereb. Cortex 25, 2919–2928 (2015). This paper delineates the developmental sequence of nine major resting-state networks during the first year of life in a cohort of full-term infants.

    Article  PubMed  Google Scholar 

  91. Doria, V. et al. Emergence of resting state networks in the preterm human brain. Proc. Natl Acad. Sci. USA 107, 20015–20020 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Fransson, P., Aden, U., Blennow, M. & Lagercrantz, H. The functional architecture of the infant brain as revealed by resting-state fMRI. Cereb. Cortex 21, 145–154 (2011).

    Article  PubMed  Google Scholar 

  93. Thomason, M. E. et al. Cross-hemispheric functional connectivity in the human fetal brain. Sci. Transl Med. 5, 173ra24 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Jones, E. G. The Thalamus (Springer Science & Business Media, 2012).

    Google Scholar 

  95. Toulmin, H. et al. Specialization and integration of functional thalamocortical connectivity in the human infant. Proc. Natl Acad. Sci. USA 112, 6485–6490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Alcauter, S. et al. Development of thalamocortical connectivity during infancy and its cognitive correlations. J. Neurosci. 34, 9067–9075 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Ball, G. et al. Thalamocortical connectivity predicts cognition in children born preterm. Cereb. Cortex 25, 4310–4318 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Shulman, G. L. et al. Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. J. Cogn. Neurosci. 9, 648–663 (1997).

    Article  CAS  PubMed  Google Scholar 

  100. Amsterdam, B. Mirror self-image reactions before age two. Dev. Psychobiol. 5, 297–305 (1972).

    Article  CAS  PubMed  Google Scholar 

  101. Emerson, R. W., Gao, W. & Lin, W. Longitudinal study of the emerging functional connectivity asymmetry of primary language regions during infancy. J. Neurosci. 36, 10883–10892 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Calhoun, V. D., Adali, T., Pearlson, G. D. & Pekar, J. J. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–151 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215 (2002).

    Article  CAS  PubMed  Google Scholar 

  104. Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Rothbart, M. K., Sheese, B. E., Rueda, M. R. & Posner, M. I. Developing mechanisms of self-regulation in early life. Emot. Rev. 3, 207–213 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Pendl, S. L. et al. Emergence of a hierarchical brain during infancy reflected by stepwise functional connectivity. Hum. Brain Mapp. 38, 2666–2682 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Gao, W. et al. The synchronization within and interaction between the default and dorsal attention networks in early infancy. Cereb. Cortex 23, 594–603 (2013).

    Article  PubMed  Google Scholar 

  109. Andersen, R. A. Multimodal integration for the representation of space in the posterior parietal cortex. Phil. Trans. R. Soc. Lond. B Biol Sci. 352, 1421–1428 (1997).

    Article  CAS  Google Scholar 

  110. Damasio, A. R. Time-locked multiregional retroactivation: a systems-level proposal for the neural substrates of recall and recognition. Cognition 33, 25–62 (1989).

    Article  CAS  PubMed  Google Scholar 

  111. Mesulam, M. M. From sensation to cognition. Brain 121, 1013–1052 (1998).

    Article  PubMed  Google Scholar 

  112. Sepulcre, J., Sabuncu, M. R., Yeo, T. B., Liu, H. & Johnson, K. A. Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain. J. Neurosci. 32, 10649–10661 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Bastos, A. M. et al. Canonical microcircuits for predictive coding. Neuron 76, 695–711 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain. Nat. Rev. Neurosci. 16, 419–429 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Gao, W. et al. Temporal and spatial evolution of brain network topology during the first two years of life. PLoS ONE 6, e25278 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Supekar, K., Musen, M. & Menon, V. Development of large-scale functional brain networks in children. PLoS Biol. 7, e1000157 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Feldman, D. E. & Brecht, M. Map plasticity in somatosensory cortex. Science 310, 810–815 (2005).

    Article  CAS  PubMed  Google Scholar 

  118. Ruigrok, A. N. et al. A meta-analysis of sex differences in human brain structure. Neurosci. Biobehav. Rev. 39, 34–50 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Sacher, J., Neumann, J., Okon-Singer, H., Gotowiec, S. & Villringer, A. Sexual dimorphism in the human brain: evidence from neuroimaging. Magn. Reson. Imag. 31, 366–375 (2013).

    Article  Google Scholar 

  120. Knickmeyer, R. C. et al. Impact of sex and gonadal steroids on neonatal brain structure. Cereb. Cortex 24, 2721–2731 (2014).

    Article  PubMed  Google Scholar 

  121. 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).

    PubMed Central  Google Scholar 

  122. Pakkenberg, B. & Gundersen, H. J. Neocortical neuron number in humans: effect of sex and age. J. Comp. Neurol. 384, 312–320 (1997).

    Article  CAS  PubMed  Google Scholar 

  123. Tanaka, C., Matsui, M., Uematsu, A., Noguchi, K. & Miyawaki, T. Developmental trajectories of the fronto-temporal lobes from infancy to early adulthood in healthy individuals. Dev. Neurosci. 34, 477–487 (2012).

    Article  CAS  PubMed  Google Scholar 

  124. Li, G. et al. Mapping longitudinal development of local cortical gyrification in infants from birth to 2 years of age. J. Neurosci. 34, 4228–4238 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Liu, Y. et al. Gender differences in language and motor-related fibers in a population of healthy preterm neonates at term-equivalent age: a diffusion tensor and probabilistic tractography study. AJNR Am. J. Neuroradiol. 32, 2011–2016 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Ratnarajah, N. et al. Structural connectivity asymmetry in the neonatal brain. Neuroimage 75, 187–194 (2013).

    Article  PubMed  Google Scholar 

  127. Deoni, S. C., Dean, D. C. III, O'Muircheartaigh, J., Dirks, H. & Jerskey, B. A. Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping. Neuroimage 63, 1038–1053 (2012).

    Article  PubMed  Google Scholar 

  128. Inano, S., Takao, H., Hayashi, N., Abe, O. & Ohtomo, K. Effects of age and gender on white matter integrity. AJNR Am. J. Neuroradiol 32, 2103–2109 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. van Hemmen, J. et al. Sex differences in white matter microstructure in the human brain predominantly reflect differences in sex hormone exposure. Cereb. Cortex 27, 2994–3001 (2017).

    CAS  PubMed  Google Scholar 

  130. Rametti, G. et al. White matter microstructure in female to male transsexuals before cross-sex hormonal treatment. A diffusion tensor imaging study. J. Psychiatr. Res. 45, 199–204 (2011).

    Article  PubMed  Google Scholar 

  131. den Braber, A. et al. Sex differences in gray and white matter structure in age-matched unrelated males and females and opposite-sex siblings. Int. J. Psychol. Res. 6, 7–21 (2013).

    Article  Google Scholar 

  132. Takao, H., Hayashi, N. & Ohtomo, K. Sex dimorphism in the white matter: fractional anisotropy and brain size. J. Magn. Reson. Imag. 39, 917–923 (2014).

    Article  Google Scholar 

  133. Menzler, K. et al. Men and women are different: diffusion tensor imaging reveals sexual dimorphism in the microstructure of the thalamus, corpus callosum and cingulum. Neuroimage 54, 2557–2562 (2011).

    Article  CAS  PubMed  Google Scholar 

  134. Kanaan, R. A. et al. Gender influence on white matter microstructure: a tract-based spatial statistics analysis. PLoS ONE 9, e91109 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Chou, K. H., Cheng, Y., Chen, I. Y., Lin, C. P. & Chu, W. C. Sex-linked white matter microstructure of the social and analytic brain. Neuroimage 54, 725–733 (2011).

    Article  PubMed  Google Scholar 

  136. Kogler, L. et al. Sex differences in the functional connectivity of the amygdalae in association with cortisol. Neuroimage 134, 410–423 (2016).

    Article  CAS  PubMed  Google Scholar 

  137. Engman, J., Linnman, C., Van Dijk, K. R. & Milad, M. R. Amygdala subnuclei resting-state functional connectivity sex and estrogen differences. Psychoneuroendocrinology 63, 34–42 (2016).

    Article  CAS  PubMed  Google Scholar 

  138. Alarcon, G., Cservenka, A., Rudolph, M. D., Fair, D. A. & Nagel, B. J. Developmental sex differences in resting state functional connectivity of amygdala sub-regions. Neuroimage 115, 235–244 (2015).

    Article  PubMed  Google Scholar 

  139. Jansen, A. G., Mous, S. E., White, T., Posthuma, D. & Polderman, T. J. What twin studies tell us about the heritability of brain development, morphology, and function: a review. Neuropsychol. Rev. 25, 27–46 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  140. Blokland, G. A., de Zubicaray, G. I., McMahon, K. L. & Wright, M. J. Genetic and environmental influences on neuroimaging phenotypes: a meta-analytical perspective on twin imaging studies. Twin Res. Hum. Genet. 15, 351–371 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Panizzon, M. S. et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728–2735 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Chen, C. H. et al. Genetic topography of brain morphology. Proc. Natl Acad. Sci. USA 110, 17089–17094 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Gilmore, J. H. et al. Genetic and environmental contributions to neonatal brain structure: a twin study. Hum. Brain Mapp. 31, 1174–1182 (2010).

    PubMed  PubMed Central  Google Scholar 

  144. Kochunov, P. et al. Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data. Neuroimage 111, 300–311 (2015).

    Article  PubMed  Google Scholar 

  145. Vuoksimaa, E. et al. Heritability of white matter microstructure in late middle age: a twin study of tract-based fractional anisotropy and absolute diffusivity indices. Hum. Brain Mapp. 38, 2026–2036 (2017).

    Article  PubMed  Google Scholar 

  146. Geng, X. et al. White matter heritability using diffusion tensor imaging in neonatal brains. Twin Res. Hum. Genet. 15, 336–350 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  147. Lee, S. J. et al. Quantitative tract-based white matter heritability in twin neonates. Neuroimage 111, 123–135 (2015).

    Article  PubMed  Google Scholar 

  148. Brouwer, R. M. et al. White matter development in early puberty: a longitudinal volumetric and diffusion tensor imaging twin study. PLoS ONE 7, e32316 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Bohlken, M. M. et al. Genes contributing to subcortical volumes and intellectual ability implicate the thalamus. Hum. Brain Mapp. 35, 2632–2642 (2014).

    Article  PubMed  Google Scholar 

  150. Glahn, D. C. et al. Genetic control over the resting brain. Proc. Natl Acad. Sci. USA 107, 1223–1228 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Schmitt, J. E. et al. The dynamic role of genetics on cortical patterning during childhood and adolescence. Proc. Natl Acad. Sci. USA 111, 6774–6779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. van den Heuvel, M. P. et al. Genetic control of functional brain network efficiency in children. Eur. Neuropsychopharmacol. 23, 19–23 (2013).

    Article  CAS  PubMed  Google Scholar 

  153. Gao, W. et al. Intersubject variability of and genetic effects on the brain's functional connectivity during infancy. J. Neurosci. 34, 11288–11296 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Silbereis, J. C., Pochareddy, S., Zhu, Y., Li, M. & Sestan, N. The cellular and molecular landscapes of the developing human central nervous system. Neuron 89, 248–268 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Shibata, M., Gulden, F. O. & Sestan, N. From trans to cis: transcriptional regulatory networks in neocortical development. Trends Genet. 31, 77–87 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Poretti, A., Boltshauser, E. & Huisman, T. A. Congenital brain abnormalities: an update on malformations of cortical development and infratentorial malformations. Semin. Neurol. 34, 239–248 (2014).

    Article  PubMed  Google Scholar 

  157. Knickmeyer, R. C. et al. Common variants in psychiatric risk genes predict brain structure at birth. Cereb. Cortex 24, 1230–1246 (2014).

    Article  PubMed  Google Scholar 

  158. Dean, D. C. et al. Brain differences in infants at differential genetic risk for late-onset Alzheimer disease: a cross-sectional imaging study. JAMA Neurol. 71, 11–22 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  159. Qiu, A. et al. COMT haplotypes modulate associations of antenatal maternal anxiety and neonatal cortical morphology. Am. J. Psychiatry 172, 163–172 (2015).

    Article  PubMed  Google Scholar 

  160. Chen, L. et al. Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism influences the association of the methylome with maternal anxiety and neonatal brain volumes. Dev. Psychopathol 27, 137–150 (2015).

    Article  CAS  PubMed  Google Scholar 

  161. Krishnan, M. L. et al. Integrative genomics of microglia implicates DLG4 (PSD95) in the white matter development of preterm infants. Nat. Commun. 8, 428 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Xia, K. et al. Genome-wide association analysis identifies common variants influencing infant brain volumes. Transl Psychiatry 7, e1188 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

    Article  CAS  PubMed  Google Scholar 

  164. Tabarki, B. et al. Severe CNS involvement in WWOX mutations: description of five new cases. Am. J. Med. Genet. A 167A, 3209–3213 (2015).

    Article  CAS  PubMed  Google Scholar 

  165. Krishnan, M. L. et al. Possible relationship between common genetic variation and white matter development in a pilot study of preterm infants. Brain Behav. 6, e00434 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  166. Brito, N. H. & Noble, K. G. Socioeconomic status and structural brain development. Front. Neurosci. 8, 276 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Farah, M. J. The neuroscience of socioeconomic status: correlates, causes, and consequences. Neuron 96, 56–71 (2017).

    Article  CAS  PubMed  Google Scholar 

  168. Luby, J. et al. The effects of poverty on childhood brain development: the mediating effect of caregiving and stressful life events. JAMA Pediatr. 167, 1135–1142 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  169. Mackey, A. P. et al. Neuroanatomical correlates of the income-achievement gap. Psychol. Sci. 26, 925–933 (2015).

    Article  PubMed  Google Scholar 

  170. Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773–778 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Hanson, J. L. et al. Family poverty affects the rate of human infant brain growth. PLoS ONE 8, e80954 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Ursache, A. & Noble, K. G. Socioeconomic status, white matter, and executive function in children. Brain Behav. 6, e00531 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  173. Buss, C. et al. Maternal cortisol over the course of pregnancy and subsequent child amygdala and hippocampus volumes and affective problems. Proc. Natl Acad. Sci. USA 109, E1312–E1319 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  174. Lebel, C. et al. Prepartum and postpartum maternal depressive symptoms are related to children's brain structure in preschool. Biol. Psychiatry 80, 859–868 (2016).

    Article  PubMed  Google Scholar 

  175. Rifkin-Graboi, A. et al. Prenatal maternal depression associates with microstructure of right amygdala in neonates at birth. Biol. Psychiatry 74, 837–844 (2013).

    Article  PubMed  Google Scholar 

  176. Qiu, A. et al. Prenatal maternal depression alters amygdala functional connectivity in 6-month-old infants. Transl Psychiatry 5, e508 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Jha, S. C. et al. Antenatal depression, treatment with selective serotonin reuptake inhibitors, and neonatal brain structure: a propensity-matched cohort study. Psychiatry Res. 253, 43–53 (2016).

    Article  PubMed Central  Google Scholar 

  178. Qiu, A. et al. Maternal anxiety and infants' hippocampal development: timing matters. Transl Psychiatry 3, e306 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  180. Derauf, C., Kekatpure, M., Neyzi, N., Lester, B. & Kosofsky, B. Neuroimaging of children following prenatal drug exposure. Semin. Cell Dev. Biol. 20, 441–454 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Donald, K. A. et al. Neuroimaging effects of prenatal alcohol exposure on the developing human brain: a magnetic resonance imaging review. Acta Neuropsychiatr. 27, 251–269 (2015).

    Article  PubMed  Google Scholar 

  182. Salzwedel, A. P., Grewen, K. M., Goldman, B. D. & Gao, W. Thalamocortical functional connectivity and behavioral disruptions in neonates with prenatal cocaine exposure. Neurotoxicol. Teratol. 56, 16–25 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Salzwedel, A. P. et al. Prenatal drug exposure affects neonatal brain functional connectivity. J. Neurosci. 35, 5860–5869 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Grewen, K., Salzwedel, A. P. & Gao, W. Functional connectivity disruption in neonates with prenatal marijuana exposure. Front. Hum. Neurosci. 9, 601 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Gabrieli, J. D., Ghosh, S. S. & Whitfield-Gabrieli, S. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Keunen, K. et al. Brain volumes at term-equivalent age in preterm infants: imaging biomarkers for neurodevelopmental outcome through early school age. J. Pediatr. 172, 88–95 (2016).

    Article  PubMed  Google Scholar 

  187. Gilmore, J. H. et al. Prenatal and neonatal brain structure and white matter maturation in children at high risk for schizophrenia. Am. J. Psychiatry 167, 1083–1091 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  188. Wolff, J. J. et al. Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am. J. Psychiatry 169, 589–600 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  189. Hazlett, H. C. et al. Early brain development in infants at high risk for autism spectrum disorder. Nature 542, 348–351 (2017). This study is an important example of how early imaging can be used to predict the development of autism.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. Graham, A. M. et al. Implications of newborn amygdala connectivity for fear and cognitive development at 6-months-of-age. Dev. Cogn. Neurosci. 18, 12–25 (2016).

    Article  PubMed  Google Scholar 

  191. Ghassabian, A. et al. Infant brain structures, executive function, and attention deficit/hyperactivity problems at preschool age. A prospective study. J. Child Psychol. Psychiatry 54, 96–104 (2013).

    Article  PubMed  Google Scholar 

  192. Herba, C. M. et al. Infant brain development and vulnerability to later internalizing difficulties: the Generation R study. J. Am. Acad. Child Adolesc. Psychiatry 49, 1053–1063 (2010).

    Article  PubMed  Google Scholar 

  193. Wee, C. Y. et al. Neonatal neural networks predict children behavioral profiles later in life. Hum. Brain Mapp. 38, 1362–1373 (2017).

    Article  PubMed  Google Scholar 

  194. Woo, C. W., Chang, L. J., Lindquist, M. A. & Wager, T. D. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20, 365–377 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Reddan, M. C., Lindquist, M. A. & Wager, T. D. Effect size estimation in neuroimaging. JAMA Psychiatry 74, 207–208 (2017).

    Article  PubMed  Google Scholar 

  196. Fjell, A. M. et al. Multimodal imaging of the self-regulating developing brain. Proc. Natl Acad. Sci. USA 109, 19620–19625 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  197. Spann, M. N., Bansal, R., Rosen, T. S. & Peterson, B. S. Morphological features of the neonatal brain support development of subsequent cognitive, language, and motor abilities. Hum. Brain Mapp. 35, 4459–4474 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  198. Short, S. J. et al. Associations between white matter microstructure and infants' working memory. Neuroimage 64, 156–166 (2013).

    Article  PubMed  Google Scholar 

  199. O'Muircheartaigh, J. et al. White matter development and early cognition in babies and toddlers. Hum. Brain Mapp. 35, 4475–4487 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  200. Deoni, S. C. et al. White matter maturation profiles through early childhood predict general cognitive ability. Brain Struct. Funct. 221, 1189–1203 (2016).

    Article  PubMed  Google Scholar 

  201. Emerson, R. W. et al. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci. Transl Med. 9 eaag2882 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  202. Smyser, C. D. et al. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 136, 1–9 (2016).

    Article  PubMed  Google Scholar 

  203. Ball, G. et al. Machine-learning to characterise neonatal functional connectivity in the preterm brain. Neuroimage 124, 267–275 (2016).

    Article  CAS  PubMed  Google Scholar 

  204. Bhardwaj, R. D. et al. Neocortical neurogenesis in humans is restricted to development. Proc. Natl Acad. Sci. USA 103, 12564–12568 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  205. Sanai, N. et al. Corridors of migrating neurons in the human brain and their decline during infancy. Nature 478, 382–386 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. Paredes, M. F. et al. Extensive migration of young neurons into the infant human frontal lobe. Science 354, aaf7073 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Conel, J. L. The Cortex of the Four-Year Child (Harvard Univ. Press, Cambridge, Massachusetts, 1963).

    Book  Google Scholar 

  208. Huttenlocher, P. R. & Dabholkar, A. S. Regional differences in synaptogenesis in human cerebral cortex. J. Comp. Neurol. 387, 167–178 (1997).

    Article  CAS  PubMed  Google Scholar 

  209. Petanjek, Z., Judas, M., Kostovic, I. & Uylings, H. B. Lifespan alterations of basal dendritic trees of pyramidal neurons in the human prefrontal cortex: a layer-specific pattern. Cereb. Cortex 18, 915–929 (2008).

    Article  PubMed  Google Scholar 

  210. Hasegawa, M. et al. Development of myelination in the human fetal and infant cerebrum: a myelin basic protein immunohistochemical study. Brain Dev. 14, 1–6 (1992).

    Article  CAS  PubMed  Google Scholar 

  211. Kinney, H. C., Brody, B. A., Kloman, A. S. & Gilles, F. H. Sequence of central nervous system myelination in human infancy II. Patterns of myelination in autopsied infants. J. Neuropathol. Exp. Neurol. 47, 217–234 (1988).

    Article  CAS  PubMed  Google Scholar 

  212. Abraham, H. et al. Myelination in the human hippocampal formation from midgestation to adulthood. Int. J. Dev. Neurosci. 28, 401–410 (2010).

    Article  CAS  PubMed  Google Scholar 

  213. Arnold, S. E. & Trojanowski, J. Q. Human fetal hippocampal development: I. Cytoarchitecture, myeloarchitecture, and neuronal morphologic features. J. Comp. Neurol. 367, 274–292 (1996).

    Article  CAS  PubMed  Google Scholar 

  214. Benes, F. M., Turtle, M., Khan, Y. & Farol, P. Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood. Arch. Gen. Psychiatry 51, 477–484 (1994).

    Article  CAS  PubMed  Google Scholar 

  215. Miller, D. J. et al. Prolonged myelination in human neocortical evolution. Proc. Natl Acad. Sci. USA 109, 16480–16485 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  216. Sigaard, R. K., Kjaer, M. & Pakkenberg, B. Development of the cell population in the brain white matter of young children. Cereb. Cortex 26, 89–95 (2016).

    Article  PubMed  Google Scholar 

  217. Yeung, M. S. et al. Dynamics of oligodendrocyte generation and myelination in the human brain. Cell 159, 766–774 (2014).

    Article  CAS  PubMed  Google Scholar 

  218. Alexander-Bloch, A. et al. Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI. Hum. Brain Mapp. 37, 2385–2397 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  219. Sairanen, V., Kuusela, L., Sipila, O., Savolainen, S. & Vanhatalo, S. A novel measure of reliability in diffusion tensor imaging after data rejections due to subject motion. Neuroimage 147, 57–65 (2017).

    Article  PubMed  Google Scholar 

  220. Power, J. D., Schlaggar, B. L. & Petersen, S. E. Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage 105, 536–551 (2015).

    Article  PubMed  Google Scholar 

  221. Reuter, M. et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage 107, 107–115 (2015).

    Article  PubMed  Google Scholar 

  222. Godenschweger, F. et al. Motion correction in MRI of the brain. Phys. Med. Biol. 61, R32–R56 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  223. Lerch, J. P. et al. Studying neuroanatomy using MRI. Nat. Neurosci. 20, 314–326 (2017).

    Article  CAS  PubMed  Google Scholar 

  224. Weinberger, D. R. & Radulescu, E. Finding the elusive psychiatric “lesion” with 21st-century neuroanatomy: a note of caution. Am. J. Psychiatry 173, 27–33 (2016).

    Article  PubMed  Google Scholar 

  225. Paus, T. et al. Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Res. Bull. 54, 255–266 (2001).

    Article  CAS  PubMed  Google Scholar 

  226. Mukherjee, P., Berman, J. I., Chung, S. W., Hess, C. P. & Henry, R. G. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR Am. J. Neuroradiol. 29, 632–641 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  227. Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61, 1000–1016 (2012).

    Article  PubMed  Google Scholar 

  228. Kunz, N. et al. Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models. Neuroimage 96, 288–299 (2014).

    Article  PubMed  Google Scholar 

  229. Jelescu, I. O. et al. One diffusion acquisition and different white matter models: how does microstructure change in human early development based on WMTI and NODDI? Neuroimage 107, 242–256 (2015).

    Article  PubMed  Google Scholar 

  230. Hutter, J. et al. Time-efficient and flexible design of optimized multishell HARDI diffusion. Magn. Reson. Med. https://doi.org/10.1002/mrm.26765 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  231. Dean, D. C. et al. Mapping white matter microstructure in the one month human brain. Sci. Rep. 7, 9759 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  232. Deoni, S. C., Rutt, B. K., Arun, T., Pierpaoli, C. & Jones, D. K. Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magn. Reson. Med. 60, 1372–1387 (2008).

    Article  PubMed  Google Scholar 

  233. Lankford, C. L. & Does, M. D. On the inherent precision of mcDESPOT. Magn. Reson. Med. 69, 127–136 (2013).

    Article  PubMed  Google Scholar 

  234. Teixeira, R. P., Malik, S. J. & Hajnal, J. V. Joint system relaxometry (JSR) and Cramer-Rao lower bound optimization of sequence parameters: a framework for enhanced precision of DESPOT T1 and T2 estimation. Magn. Reson. Med. 79, 234–245 (2018).

    Article  CAS  PubMed  Google Scholar 

  235. Wozniak, J. R. & Lim, K. O. Advances in white matter imaging: a review of in vivo magnetic resonance methodologies and their applicability to the study of development and aging. Neurosci. Biobehav Rev. 30, 762–774 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  236. Jones, D. K. & Cercignani, M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 23, 803–820 (2010).

    Article  PubMed  Google Scholar 

  237. Jones, D. K., Knosche, T. R. & Turner, R. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. Neuroimage 73, 239–254 (2013).

    Article  PubMed  Google Scholar 

  238. Mukherjee, P., Chung, S. W., Berman, J. I., Hess, C. P. & Henry, R. G. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am. J. Neuroradiol 29, 843–852 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  239. Dubois, J. et al. Correction strategy for diffusion-weighted images corrupted with motion: application to the DTI evaluation of infants' white matter. Magn. Reson. Imag. 32, 981–992 (2014).

    Article  Google Scholar 

  240. Berger, H. Über das Elektrenkephalogramm des Menschen. Archiv. Psychiatrie Nervenkrankheiten 87, 527–570 (1929).

    Article  Google Scholar 

  241. Cohen, D. Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer. Science 175, 664–666 (1972).

    Article  CAS  PubMed  Google Scholar 

  242. Born, P., Rostrup, E., Leth, H., Peitersen, B. & Lou, H. C. Change of visually induced cortical activation patterns during development. Lancet 347, 543 (1996).

    Article  CAS  PubMed  Google Scholar 

  243. Born, P. et al. Visual activation in infants and young children studied by functional magnetic resonance imaging. Pediatr. Res. 44, 578–583 (1998).

    Article  CAS  PubMed  Google Scholar 

  244. Meek, J. H. et al. Regional hemodynamic responses to visual stimulation in awake infants. Pediatr. Res. 43, 840–843 (1998).

    Article  CAS  PubMed  Google Scholar 

  245. Nir, Y. et al. Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nat Neurosci. 11, 1100–1108 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  246. Shi, F., Salzwedel, A. P., Lin, W., Gilmore, J. H. & Gao, W. Functional brain parcellations of the infant brain and the associated developmental trends. Cereb. Cortex https://doi.org/10.1093/cercor/bhx062 (2017).

    Article  PubMed Central  Google Scholar 

  247. Ciric, R. et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage 154, 174–187 (2017).

    Article  PubMed  Google Scholar 

  248. Bullins, J., Jha, S. C., Knickmeyer, R. & Gilmore, J. in Handbook of Preschool Mental Health: Development, Disorders, and Treatment (ed. Luby, J. L.) 73–97 (The Guilford Press, New York, 2017).

    Google Scholar 

  249. Smith, S. M. et al. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 13040–13045 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank members of the University of North Carolina Early Brain Development Laboratory for their help with the manuscript and figures and M. Styner and D. Rubinow for their thoughtful reading and comments. This work is supported by the US National Institutes of Health: grants MH070890, HD053000 and MH111944 to J.H.G.; grants NS088975, DA043171 and DA036645 to W.G.; and grants MH092335 and MH104330 to R.C.K.

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Authors and Affiliations

  1. Department of Psychiatry, CB# 7160, University of North Carolina School of Medicine, Chapel Hill, NC, USA

    John H. Gilmore & Rebecca C. Knickmeyer

  2. Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Wei Gao

  3. Department of Medicine, University of California, Los Angeles, CA, USA

    Wei Gao

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The authors all researched data for the article, provided a substantial contribution to discussion of the content, wrote the article and reviewed and edited the manuscript before submission.

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Correspondence to John H. Gilmore.

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Glossary

Axial diffusivity

(AD). Water diffusion along the principal axis of a fibre, thought to be dependent on fibre compactness and microstructure.

Radial diffusivity

(RD). Water diffusion perpendicular to the main axis of the fibre, sensitive to myelination.

Fractional anisotropy

(FA). A summary measure of microstructure that takes into account axial diffusivity and radial diffusivity.

Network integration

The overall capacity of the network to be interconnected and exchange information.

Network segregation

The degree to which parts of a network form localized clusters of nodes or modules of connections.

Structural covariance networks

(SCNs). Regions of cortical grey matter with correlated variance in cortical thickness.

Maturational networks

Regions of cortical grey matter with correlated changes in cortical thickness over time.

Core structure

The collection of key hub regions that possess the most connections that bring the whole network together.

Dorsal attention network

The network involved with the 'top–down', or voluntary, focusing of attention; includes the intraparietal sulcus, frontal eye fields and middle temporal regions.

Salience network

The network involved with the selection of relevant stimuli; includes the insula, anterior cingulate cortex and amygdala.

Step-wise analysis

A technique that attempts to identify intermediate connector regions and multistep links between two brain regions that do not show directly correlated functional activity.

Predictive encoding

Theoretical framework in which higher-level cortices continuously generate predictions about the environment on the basis of learned input regularities, to minimize errors between lower-level inputs and predictions.

Heritability

The proportion of variance in a trait or measure that is due to genetic variation.

Path length

A measure of efficiency in a network; the average number of edges along the shortest paths connecting all pairs of nodes within a network.

Clustering coefficient

A measure of the proportion of existing links between one node's neighbours divided by the total number of links for a fully connected neighbourhood. It reflects how densely one node and its neighbours are locally connected.

Methylome

The pattern of DNA methylation within a genome.

Internalizing behaviour

Problem behaviours that are typically directed inward, such as anxiety, depression, social withdrawal and somatic symptoms.

Externalizing behaviour

Problem behaviours that are directed outward towards others, such as physical aggression, defiance, hyperactivity, bullying and theft.

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Gilmore, J., Knickmeyer, R. & Gao, W. Imaging structural and functional brain development in early childhood. Nat Rev Neurosci 19, 123–137 (2018). https://doi.org/10.1038/nrn.2018.1

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