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
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.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Nelson, C. A. et al. Cognitive recovery in socially deprived young children: the Bucharest Early Intervention Project. Science 318, 1937–1940 (2007).
National Advisory Mental Health Council's Workgroup.Transformative Neurodevelopmental Research in Mental Illness (US National Institute of Mental Health, 2008).
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.
Lyall, A. E. et al. Dynamic development of regional cortical thickness and surface area in early childhood. Cereb. Cortex 25, 2204–2212 (2015).
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.
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).
Petanjek, Z. et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc. Natl Acad. Sci. USA 108, 13281–13286 (2011).
Pletikos, M. et al. Temporal specification and bilaterality of human neocortical topographic gene expression. Neuron 81, 321–332 (2014).
Geng, X. et al. Structural and maturational covariance in early childhood brain development. Cereb. Cortex 27, 1795–1807 (2017).
Gilmore, J. H. et al. Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb. Cortex 22, 2478–2485 (2012).
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).
Jakab, A. et al. Fetal cerebral magnetic resonance imaging beyond morphology. Semin. Ultrasound CT MR 36, 465–475 (2015).
Gilmore, J. H. et al. Regional gray matter growth, sexual dimorphism, and cerebral asymmetry in the neonatal brain. J. Neurosci. 27, 1255–1260 (2007).
Courchesne, E. et al. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 216, 672–682 (2000).
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).
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).
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).
Mills, K. L. et al. Structural brain development between childhood and adulthood: convergence across four longitudinal samples. Neuroimage 141, 273–281 (2016).
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).
Raznahan, A. et al. How does your cortex grow? J. Neurosci. 31, 7174–7177 (2011).
Shaw, P. et al. Neurodevelopmental trajectories of the human cerebral cortex. J. Neurosci. 28, 3586–3594 (2008).
Remer, J. et al. Quantifying cortical development in typically developing toddlers and young children, 1–6 years of age. Neuroimage 153, 246–261 (2017).
Li, G. et al. Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. Cereb. Cortex 23, 2724–2733 (2013).
Hill, J. et al. Similar patterns of cortical expansion during human development and evolution. Proc. Natl Acad. Sci. USA 107, 13135–13140 (2010).
Brown, T. T. et al. Neuroanatomical assessment of biological maturity. Curr. Biol. 22, 1693–1698 (2012).
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).
Ducharme, S. et al. Trajectories of cortical thickness maturation in normal brain development — the importance of quality control procedures. Neuroimage 125, 267–279 (2016).
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).
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).
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).
Qiu, A., Mori, S. & Miller, M. I. Diffusion tensor imaging for understanding brain development in early life. Annu. Rev. Psychol. 66, 853–876 (2015).
Geng, X. et al. Quantitative tract-based white matter development from birth to age 2 years. Neuroimage 61, 542–557 (2012).
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.
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).
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).
Dean, D. C. et al. Modeling healthy male white matter and myelin development: 3 through 60 months of age. Neuroimage 84, 742–752 (2014).
Dean, D. C. et al. Characterizing longitudinal white matter development during early childhood. Brain Struct. Funct. 220, 1921–1933 (2015).
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).
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).
Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336–349 (2012).
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).
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).
Cao, M., Huang, H. & He, Y. Developmental connectomics from infancy through early childhood. Trends Neurosci. 40, 494–506 (2017).
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).
Yan, G. et al. Network control principles predict neuron function in the Caenorhabditis elegans connectome. Nature 550, 519–523 (2017).
Cao, M., Huang, H., Peng, Y., Dong, Q. & He, Y. Toward developmental connectomics of the human brain. Front. Neuroanat. 10, 25 (2016).
Yap, P. T. et al. Development trends of white matter connectivity in the first years of life. PLoS ONE 6, e24678 (2011).
van den Heuvel, M. P. et al. The neonatal connectome during preterm brain development. Cereb. Cortex 25, 3000–3013 (2015).
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.
Huang, H. et al. Development of human brain structural networks through infancy and childhood. Cereb. Cortex 25, 1389–1404 (2015).
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).
Evans, A. C. Networks of anatomical covariance. Neuroimage 80, 489–504 (2013).
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).
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).
Alexander-Bloch, A., Giedd, J. N. & Bullmore, E. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322–336 (2013).
Khundrakpam, B. S. et al. Imaging structural covariance in the development of intelligence. Neuroimage 144, 227–240 (2017).
Fan, Y. et al. Brain anatomical networks in early human brain development. Neuroimage 54, 1862–1871 (2011).
Nie, J., Li, G. & Shen, D. Development of cortical anatomical properties from early childhood to early adulthood. Neuroimage 76, 216–224 (2013).
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).
Khundrakpam, B. S. et al. Developmental changes in organization of structural brain networks. Cereb. Cortex 23, 2072–2085 (2013).
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).
Dehaene-Lambertz, G. & Spelke, E. S. The infancy of the human brain. Neuron 88, 93–109 (2015).
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).
Anderson, C. M., Torres, F. & Faoro, A. The EEG of the early premature. Electroencephalogr. Clin. Neurophysiol. 60, 95–105 (1985).
Arichi, T. et al. Localization of spontaneous bursting neuronal activity in the preterm human brain with simultaneous EEG-fMRI. eLife 6, e27814 (2017).
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).
Ackman, J. B., Burbridge, T. J. & Crair, M. C. Retinal waves coordinate patterned activity throughout the developing visual system. Nature 490, 219–225 (2012).
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).
Blumberg, M. S. Developing sensorimotor systems in our sleep. Curr. Direct. Psychol. Sci. 24, 32–37 (2015).
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).
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).
Jobsis, F. F. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198, 1264–1267 (1977).
Arichi, T. et al. Development of BOLD signal hemodynamic responses in the human brain. Neuroimage 63, 663–673 (2012).
Allievi, A. G. et al. Maturation of sensori-motor functional responses in the preterm brain. Cereb. Cortex 26, 402–413 (2016).
Karen, T. et al. Hemodynamic response to visual stimulation in newborn infants using functional near-infrared spectroscopy. Hum. Brain Mapp. 29, 453–460 (2008).
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.
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).
Wilcox, T., Stubbs, J., Hirshkowitz, A. & Boas, D. Object processing and functional organization of the infant cortex. Neuroimage 62, 1833–1840 (2012).
Nakano, T., Watanabe, H., Homae, F. & Taga, G. Prefrontal cortical involvement in young infants' analysis of novelty. Cereb. Cortex 19, 455–463 (2009).
Grossmann, T. & Johnson, M. H. Selective prefrontal cortex responses to joint attention in early infancy. Biol. Lett. 6, 540–543 (2010).
Kozberg, M. & Hillman, E. Neurovascular coupling and energy metabolism in the developing brain. Prog. Brain Res. 225, 213–242 (2016).
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).
Gao, W., Lin, W., Grewen, K. & Gilmore, J. H. Functional connectivity of the infant human brain: plastic and modifiable. Neuroscientist 23, 169–184 (2016).
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.
Lin, W. et al. Functional connectivity MR imaging reveals cortical functional connectivity in the developing brain. AJNR Am. J. Neuroradiol. 29, 1883–1889 (2008).
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).
Smyser, C. D. et al. Longitudinal analysis of neural network development in preterm infants. Cereb. Cortex 20, 2852–2862 (2010).
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).
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.
Doria, V. et al. Emergence of resting state networks in the preterm human brain. Proc. Natl Acad. Sci. USA 107, 20015–20020 (2010).
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).
Thomason, M. E. et al. Cross-hemispheric functional connectivity in the human fetal brain. Sci. Transl Med. 5, 173ra24 (2013).
Jones, E. G. The Thalamus (Springer Science & Business Media, 2012).
Toulmin, H. et al. Specialization and integration of functional thalamocortical connectivity in the human infant. Proc. Natl Acad. Sci. USA 112, 6485–6490 (2015).
Alcauter, S. et al. Development of thalamocortical connectivity during infancy and its cognitive correlations. J. Neurosci. 34, 9067–9075 (2014).
Ball, G. et al. Thalamocortical connectivity predicts cognition in children born preterm. Cereb. Cortex 25, 4310–4318 (2015).
Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001).
Shulman, G. L. et al. Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. J. Cogn. Neurosci. 9, 648–663 (1997).
Amsterdam, B. Mirror self-image reactions before age two. Dev. Psychobiol. 5, 297–305 (1972).
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).
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).
Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215 (2002).
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).
Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356 (2007).
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).
Pendl, S. L. et al. Emergence of a hierarchical brain during infancy reflected by stepwise functional connectivity. Hum. Brain Mapp. 38, 2666–2682 (2017).
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).
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).
Damasio, A. R. Time-locked multiregional retroactivation: a systems-level proposal for the neural substrates of recall and recognition. Cognition 33, 25–62 (1989).
Mesulam, M. M. From sensation to cognition. Brain 121, 1013–1052 (1998).
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).
Bastos, A. M. et al. Canonical microcircuits for predictive coding. Neuron 76, 695–711 (2012).
Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain. Nat. Rev. Neurosci. 16, 419–429 (2015).
Gao, W. et al. Temporal and spatial evolution of brain network topology during the first two years of life. PLoS ONE 6, e25278 (2011).
Supekar, K., Musen, M. & Menon, V. Development of large-scale functional brain networks in children. PLoS Biol. 7, e1000157 (2009).
Feldman, D. E. & Brecht, M. Map plasticity in somatosensory cortex. Science 310, 810–815 (2005).
Ruigrok, A. N. et al. A meta-analysis of sex differences in human brain structure. Neurosci. Biobehav. Rev. 39, 34–50 (2014).
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).
Knickmeyer, R. C. et al. Impact of sex and gonadal steroids on neonatal brain structure. Cereb. Cortex 24, 2721–2731 (2014).
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).
Pakkenberg, B. & Gundersen, H. J. Neocortical neuron number in humans: effect of sex and age. J. Comp. Neurol. 384, 312–320 (1997).
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).
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).
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).
Ratnarajah, N. et al. Structural connectivity asymmetry in the neonatal brain. Neuroimage 75, 187–194 (2013).
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).
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).
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).
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).
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).
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).
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).
Kanaan, R. A. et al. Gender influence on white matter microstructure: a tract-based spatial statistics analysis. PLoS ONE 9, e91109 (2014).
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).
Kogler, L. et al. Sex differences in the functional connectivity of the amygdalae in association with cortisol. Neuroimage 134, 410–423 (2016).
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).
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).
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).
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).
Panizzon, M. S. et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728–2735 (2009).
Chen, C. H. et al. Genetic topography of brain morphology. Proc. Natl Acad. Sci. USA 110, 17089–17094 (2013).
Gilmore, J. H. et al. Genetic and environmental contributions to neonatal brain structure: a twin study. Hum. Brain Mapp. 31, 1174–1182 (2010).
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).
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).
Geng, X. et al. White matter heritability using diffusion tensor imaging in neonatal brains. Twin Res. Hum. Genet. 15, 336–350 (2012).
Lee, S. J. et al. Quantitative tract-based white matter heritability in twin neonates. Neuroimage 111, 123–135 (2015).
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).
Bohlken, M. M. et al. Genes contributing to subcortical volumes and intellectual ability implicate the thalamus. Hum. Brain Mapp. 35, 2632–2642 (2014).
Glahn, D. C. et al. Genetic control over the resting brain. Proc. Natl Acad. Sci. USA 107, 1223–1228 (2010).
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).
van den Heuvel, M. P. et al. Genetic control of functional brain network efficiency in children. Eur. Neuropsychopharmacol. 23, 19–23 (2013).
Gao, W. et al. Intersubject variability of and genetic effects on the brain's functional connectivity during infancy. J. Neurosci. 34, 11288–11296 (2014).
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).
Shibata, M., Gulden, F. O. & Sestan, N. From trans to cis: transcriptional regulatory networks in neocortical development. Trends Genet. 31, 77–87 (2015).
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).
Knickmeyer, R. C. et al. Common variants in psychiatric risk genes predict brain structure at birth. Cereb. Cortex 24, 1230–1246 (2014).
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).
Qiu, A. et al. COMT haplotypes modulate associations of antenatal maternal anxiety and neonatal cortical morphology. Am. J. Psychiatry 172, 163–172 (2015).
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).
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).
Xia, K. et al. Genome-wide association analysis identifies common variants influencing infant brain volumes. Transl Psychiatry 7, e1188 (2017).
Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).
Tabarki, B. et al. Severe CNS involvement in WWOX mutations: description of five new cases. Am. J. Med. Genet. A 167A, 3209–3213 (2015).
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).
Brito, N. H. & Noble, K. G. Socioeconomic status and structural brain development. Front. Neurosci. 8, 276 (2014).
Farah, M. J. The neuroscience of socioeconomic status: correlates, causes, and consequences. Neuron 96, 56–71 (2017).
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).
Mackey, A. P. et al. Neuroanatomical correlates of the income-achievement gap. Psychol. Sci. 26, 925–933 (2015).
Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773–778 (2015).
Hanson, J. L. et al. Family poverty affects the rate of human infant brain growth. PLoS ONE 8, e80954 (2013).
Ursache, A. & Noble, K. G. Socioeconomic status, white matter, and executive function in children. Brain Behav. 6, e00531 (2016).
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).
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).
Rifkin-Graboi, A. et al. Prenatal maternal depression associates with microstructure of right amygdala in neonates at birth. Biol. Psychiatry 74, 837–844 (2013).
Qiu, A. et al. Prenatal maternal depression alters amygdala functional connectivity in 6-month-old infants. Transl Psychiatry 5, e508 (2015).
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).
Qiu, A. et al. Maternal anxiety and infants' hippocampal development: timing matters. Transl Psychiatry 3, e306 (2013).
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).
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).
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).
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).
Salzwedel, A. P. et al. Prenatal drug exposure affects neonatal brain functional connectivity. J. Neurosci. 35, 5860–5869 (2015).
Grewen, K., Salzwedel, A. P. & Gao, W. Functional connectivity disruption in neonates with prenatal marijuana exposure. Front. Hum. Neurosci. 9, 601 (2015).
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).
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).
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).
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).
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.
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).
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).
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).
Wee, C. Y. et al. Neonatal neural networks predict children behavioral profiles later in life. Hum. Brain Mapp. 38, 1362–1373 (2017).
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).
Reddan, M. C., Lindquist, M. A. & Wager, T. D. Effect size estimation in neuroimaging. JAMA Psychiatry 74, 207–208 (2017).
Fjell, A. M. et al. Multimodal imaging of the self-regulating developing brain. Proc. Natl Acad. Sci. USA 109, 19620–19625 (2012).
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).
Short, S. J. et al. Associations between white matter microstructure and infants' working memory. Neuroimage 64, 156–166 (2013).
O'Muircheartaigh, J. et al. White matter development and early cognition in babies and toddlers. Hum. Brain Mapp. 35, 4475–4487 (2014).
Deoni, S. C. et al. White matter maturation profiles through early childhood predict general cognitive ability. Brain Struct. Funct. 221, 1189–1203 (2016).
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).
Smyser, C. D. et al. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 136, 1–9 (2016).
Ball, G. et al. Machine-learning to characterise neonatal functional connectivity in the preterm brain. Neuroimage 124, 267–275 (2016).
Bhardwaj, R. D. et al. Neocortical neurogenesis in humans is restricted to development. Proc. Natl Acad. Sci. USA 103, 12564–12568 (2006).
Sanai, N. et al. Corridors of migrating neurons in the human brain and their decline during infancy. Nature 478, 382–386 (2011).
Paredes, M. F. et al. Extensive migration of young neurons into the infant human frontal lobe. Science 354, aaf7073 (2016).
Conel, J. L. The Cortex of the Four-Year Child (Harvard Univ. Press, Cambridge, Massachusetts, 1963).
Huttenlocher, P. R. & Dabholkar, A. S. Regional differences in synaptogenesis in human cerebral cortex. J. Comp. Neurol. 387, 167–178 (1997).
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).
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).
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).
Abraham, H. et al. Myelination in the human hippocampal formation from midgestation to adulthood. Int. J. Dev. Neurosci. 28, 401–410 (2010).
Arnold, S. E. & Trojanowski, J. Q. Human fetal hippocampal development: I. Cytoarchitecture, myeloarchitecture, and neuronal morphologic features. J. Comp. Neurol. 367, 274–292 (1996).
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).
Miller, D. J. et al. Prolonged myelination in human neocortical evolution. Proc. Natl Acad. Sci. USA 109, 16480–16485 (2012).
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).
Yeung, M. S. et al. Dynamics of oligodendrocyte generation and myelination in the human brain. Cell 159, 766–774 (2014).
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).
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).
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).
Reuter, M. et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage 107, 107–115 (2015).
Godenschweger, F. et al. Motion correction in MRI of the brain. Phys. Med. Biol. 61, R32–R56 (2016).
Lerch, J. P. et al. Studying neuroanatomy using MRI. Nat. Neurosci. 20, 314–326 (2017).
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).
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).
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).
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).
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).
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).
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).
Dean, D. C. et al. Mapping white matter microstructure in the one month human brain. Sci. Rep. 7, 9759 (2017).
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).
Lankford, C. L. & Does, M. D. On the inherent precision of mcDESPOT. Magn. Reson. Med. 69, 127–136 (2013).
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).
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).
Jones, D. K. & Cercignani, M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 23, 803–820 (2010).
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).
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).
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).
Berger, H. Über das Elektrenkephalogramm des Menschen. Archiv. Psychiatrie Nervenkrankheiten 87, 527–570 (1929).
Cohen, D. Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer. Science 175, 664–666 (1972).
Born, P., Rostrup, E., Leth, H., Peitersen, B. & Lou, H. C. Change of visually induced cortical activation patterns during development. Lancet 347, 543 (1996).
Born, P. et al. Visual activation in infants and young children studied by functional magnetic resonance imaging. Pediatr. Res. 44, 578–583 (1998).
Meek, J. H. et al. Regional hemodynamic responses to visual stimulation in awake infants. Pediatr. Res. 43, 840–843 (1998).
Nir, Y. et al. Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nat Neurosci. 11, 1100–1108 (2008).
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).
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).
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).
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).
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.
The authors declare no competing financial interests.
- 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.
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.
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.
About this article
Cite this article
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
Effects of maternal folic acid supplementation during the second and third trimesters of pregnancy on neurocognitive development in the child: an 11-year follow-up from a randomised controlled trial
BMC Medicine (2021)
Impact of parent-child separation on children’s social-emotional development: a cross-sectional study of left-behind children in poor rural areas of China
BMC Public Health (2021)
npj Science of Learning (2021)
Charting Brain Development in Graphs, Diagrams, and Figures from Childhood, Adolescence, to Early Adulthood: Neuroimaging Implications for Neuropsychology
Journal of Pediatric Neuropsychology (2021)
Something Scary Is Out There: Remembrances of Where the Threat Was Located by Preschool Children and Adults with Nighttime Fear
Evolutionary Psychological Science (2021)