Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-03T05:27:45.447Z Has data issue: false hasContentIssue false

4 - Development and Maturation of the Human Brain, from Infancy to Adolescence

from Part I - Neurobiological Constraints and Laws of Cognitive Development

Published online by Cambridge University Press:  24 February 2022

By
Tomáš Paus
Edited by
Olivier Houdé  and
Grégoire Borst
Olivier Houdé
Affiliation:
Université de Paris V
Grégoire Borst
Affiliation:
Université de Paris V
Get access

Summary

This chapter describes basic principles and key findings regarding the development and maturation of the human brain, the former referring to the pre-natal and early post-natal periods, and the latter concerning childhood and adolescence. In both cases, we focus on brain structure as revealed in vivo with multi-modal magnetic resonance imaging (MRI). We begin with a few numbers about the human brain and its cellular composition, and a brief overview of a number of MRI-based metrics used to characterize age-related variations in grey and white matter. We then proceed with synthesizing current knowledge about developmental and maturational changes in the cerebral cortex (its thickness, surface area and intra-cortical myelination), and the underlying white matter (volume and structural properties). To facilitate biological interpretations of MRI-derived metrics, we introduce the concept of virtual histology. We conclude the chapter with a few notes about future directions in the study of factors shaping the human brain from conception onwards.

Type
Chapter
Information
The Cambridge Handbook of Cognitive Development , pp. 85 - 104
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abnousi, F., Krumholz, H. M., & Rumsfeld, J. S. (2018). Social determinants of health in the digital age: Determining the source code for nurture. JAMA, 321, 247–248.CrossRefGoogle Scholar
Aboitiz, F., Scheibel, A. B., Fisher, R. S., & Zaidel, E. (1992). Fiber composition of the human corpus callosum. Brain Research, 598, 143–153.Google Scholar
Azevedo, F. A., Carvalho, L. R., Grinberg, L. T., et al. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. The Journal of Comparative Neurology, 513, 532–541.CrossRefGoogle Scholar
Bjornholm, L., Nikkinen, J., Kiviniemi, V., et al. (2017). Structural properties of the human corpus callosum: Multimodal assessment and sex differences. Neuroimage, 152, 108–118.CrossRefGoogle ScholarPubMed
Brain Development Cooperative Group (2012). Total and regional brain volumes in a population-based normative sample from 4 to 18 years: The NIH MRI Study of Normal Brain Development. Cerebral Cortex, 22, 1–12.CrossRefGoogle Scholar
Bystron, I., Blakemore, C., Rakic, P., & Molnar, Z. (2006). The first neurons of the human cerebral cortex. Nature Neuroscience, 9, 880–886.CrossRefGoogle ScholarPubMed
Davey-Smith, G., & Hemani, G. (2014). Mendelian randomization: Genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 23, R89–R98.CrossRefGoogle ScholarPubMed
David-Barrett, T., Kertesz, J., Rotkirch, A., et al. (2016). Communication with family and friends across the life course. PLoS ONE, 11, e0165687.CrossRefGoogle ScholarPubMed
Dean, D. C., 3rd, O’Muircheartaigh, J., Dirks, H., et al. (2015). Characterizing longitudinal white matter development during early childhood. Brain Structure and Function, 220, 1921–1933.CrossRefGoogle ScholarPubMed
Druga, R. (2009). Neocortical inhibitory system. Folia Biologica, 55, 201–17.Google ScholarPubMed
Ducharme, S., Albaugh, M. D., Nguyen, T. V., et al. (2016). Trajectories of cortical thickness maturation in normal brain development – The importance of quality control procedures. Neuroimage, 125, 267.CrossRefGoogle ScholarPubMed
Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences (USA), 97, 11050–11055.CrossRefGoogle ScholarPubMed
French, L., & Paus, T. A. (2015). FreeSurfer view of the cortical transcriptome generated from the Allen Human Brain Atlas. Frontiers in Neuroscience, 9, 323.CrossRefGoogle ScholarPubMed
Garavan, H., Bartsch, H., Conway, K., et al. (2018). Recruiting the ABCD sample: Design considerations and procedures. Developmental Cognitive Neuroscience, 32, 16–22.Google Scholar
Giedd, J. N., Blumenthal, J., Jeffries, N. O., et al. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2, 861–863.CrossRefGoogle ScholarPubMed
Giedd, J. N., Raznahan, A., Alexander-Bloch, A., et al. (2015). Child psychiatry branch of the National Institute of Mental Health longitudinal structural magnetic resonance imaging study of human brain development. Neuropsychopharmacology, 40, 43–49.CrossRefGoogle ScholarPubMed
Gilmore, J. H., Knickmeyer, R. C., & Gao, W. (2018). Imaging structural and functional brain development in early childhood. Nature Reviews Neuroscience, 19, 123–137.CrossRefGoogle ScholarPubMed
Gogtay, N., Giedd, J. N., Lusk, L., et al. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences (USA), 101, 8174–8179.CrossRefGoogle ScholarPubMed
Gruzd, A., & Haythornthwaite, C. (2013). Enabling community through social media. Journal of Medical Internet Research, 15, e248.CrossRefGoogle ScholarPubMed
Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., et al. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489, 391–399.Google Scholar
Henkelman, R. M., Stanisz, G. J., & Graham, S. J. (2001). Magnetization transfer in MRI: A review. NMR in Biomedicine, 14, 57–64.Google Scholar
Herculano-Houzel, S. (2009). The human brain in numbers: A linearly scaled-up primate brain. Frontiers in Human Neuroscience, 3, 31.CrossRefGoogle Scholar
Hermann, D. M., Buga, A. M., & Popa-Wagner, A. (2015). Neurovascular remodeling in the aged ischemic brain. Journal of Neural Transmission (Vienna), 122(Suppl 1), S25–S33.CrossRefGoogle ScholarPubMed
Herve, P. Y., Cox, E. F., Lotfipour, A. K., et al. (2011). Structural properties of the corticospinal tract in the human brain: A magnetic resonance imaging study at 7 Tesla. Brain Structure and Function, 216, 255–262.CrossRefGoogle Scholar
Herve, P. Y., Leonard, G., Perron, M., et al. (2009). Handedness, motor skills and maturation of the corticospinal tract in the adolescent brain. Human Brain Mapping, 30, 3151–3162.CrossRefGoogle ScholarPubMed
Houdé, O., Zago, L., Mellet, E., et al. (2000). Shifting from the perceptual brain to the logical brain: The neural impact of cognitive inhibition training. Journal of Cognitive Neuroscience, 12, 721–728.CrossRefGoogle Scholar
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van-Essen, D. C., & Behrens, T. E. (2015). Measuring macroscopic brain connections in vivo. Nature Neuroscience, 18, 1546–1555.CrossRefGoogle ScholarPubMed
Jernigan, T. L., Brown, T. T., Hagler, D. J. Jr., et al. (2016). The Pediatric Imaging, Neurocognition, and Genetics (PING) data repository. Neuroimage, 124, 1149–1154.CrossRefGoogle ScholarPubMed
Kardan, O., Gozdyra, P., Misic, B., et al. (2015). Neighborhood greenspace and health in a large urban center. Scientific Reports, 5, 11610.CrossRefGoogle Scholar
Knickmeyer, R. C., Gouttard, S., Kang, C., et al. (2008). A structural MRI study of human brain development from birth to 2 years. Journal of Neuroscience, 28, 12176–12182.CrossRefGoogle ScholarPubMed
Kramer, M. S., Aboud, F., Mironova, E., et al. (2008). Breastfeeding and child cognitive development: New evidence from a large randomized trial. Archives of General Psychiatry, 65, 578–584.CrossRefGoogle ScholarPubMed
Kucharczyk, W., Macdonald, P. M., Stanisz, G. J., & Henkelman, R. M. (1994). Relaxivity and magnetization transfer of white matter lipids at MR imaging: Importance of cerebrosides and pH. Radiology, 192, 521–529.CrossRefGoogle ScholarPubMed
Kum, H. C., Krishnamurthy, A., Machanavajjhala, A., & Ahalt, S. (2014). Social genome: Putting big data to work for population informatics. Computer 47, 56–63.CrossRefGoogle Scholar
Le Bihan, D. (1995). Molecular diffusion, tissue microdynamics and microstructure. NMR Biomedicine, 8, 375–386.CrossRefGoogle ScholarPubMed
Lebel, C., & Deoni, S. (2018). The development of brain white matter microstructure. Neuroimage, 182, 207–218.CrossRefGoogle ScholarPubMed
Lerch, J. P., Van der Kouwe, A. J., Raznahan, A., et al. (2017). Studying neuroanatomy using MRI. Nature Neuroscience, 20, 314–326.CrossRefGoogle ScholarPubMed
Maharana, A., & Okanyene Nsoesie, E. (2018). Use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity. JAMA Network Open, 1, e181535.CrossRefGoogle ScholarPubMed
Marner, L., & Pakkenberg, B. (2003). Total length of nerve fibers in prefrontal and global white matter of chronic schizophrenics. Journal of Psychiatric Research, 37, 539–547.CrossRefGoogle ScholarPubMed
Matsuzawa, J., Matsui, M., Konishi, T., et al. (2001). Age-related volumetric changes of brain gray and white matter in healthy infants and children. Cerebral Cortex, 11, 335–342.CrossRefGoogle ScholarPubMed
McGowan, J. C. (1999). The physical basis of magnetization transfer imaging. Neurology, 53, S3–S7.Google ScholarPubMed
Moore, M. M., & Chung, T. (2017). Review of key concepts in magnetic resonance physics. Pediatric Radiology, 47, 497–506.CrossRefGoogle ScholarPubMed
Morales, A. J., Vavilala, V., Benito, R. M., & Bar-Yam, Y. (2017). Global patterns of synchronization in human communications. Journal of the Royal Society, Interface, 14, 20161048.CrossRefGoogle ScholarPubMed
Pangelinan, M. M., Leonard, G., Perron, M., et al. (2016). Puberty and testosterone shape the corticospinal tract during male adolescence. Brain Structure and Function, 221, 1083–1094.CrossRefGoogle ScholarPubMed
Parker, N., Wong, A. P., Leonard, G., et al. (2017). Income inequality, gene expression, and brain maturation during adolescence. Scientific Reports, 7, 7397.CrossRefGoogle ScholarPubMed
Patel, Y., Shin, J., Gowland, P. A., et al. (2019). Maturation of the human cerebral cortex during adolescence: Myelin or dendritic arbor? Cerebral Cortex, 29, 3351–3362.CrossRefGoogle ScholarPubMed
Paus, T. (2005). Mapping brain maturation and cognitive development during adolescence. Trends in Cognitive Science, 9, 60–68.CrossRefGoogle ScholarPubMed
Paus, T. (2013). Population Neuroscience. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Paus, T. (2016). Population neuroscience. Handbook of Clinical Neurology, 138, 17–37.CrossRefGoogle ScholarPubMed
Paus, T. (2018). Imaging microstructure in the living human brain: A viewpoint. Neuroimage, 182, 3–7.CrossRefGoogle Scholar
Paus, T., Collins, D. L., Evans, A. C., et al. (2001). Maturation of white matter in the human brain: A review of magnetic resonance studies. Brain Research Bulletin, 54, 255–266.CrossRefGoogle Scholar
Paus, T., Pesaresi, M., & French, L. (2014). White matter as a transport system. Neuroscience, 276, 117–125.CrossRefGoogle ScholarPubMed
Paus, T., & Toro, R. (2009). Could sex differences in white matter be explained by g ratio? Frontiers in Neuroanatomy, 3, 14.CrossRefGoogle ScholarPubMed
Pausova, Z., Paus, T., Abrahamowicz, M., et al. (2007). Genes, maternal smoking, and the offspring brain and body during adolescence: Design of the Saguenay youth study. Human Brain Mapping, 28, 502–518.CrossRefGoogle ScholarPubMed
Pausova, Z., Paus, T., Abrahamowicz, M., et al. (2017). Cohort profile: The Saguenay Youth Study (SYS). International Journal of Epidemiology, 46, e19.Google ScholarPubMed
Pelvig, D. P., Pakkenberg, H., Stark, A. K., & Pakkenberg, B. (2008). Neocortical glial cell numbers in human brains. Neurobiology of Aging, 29, 1754–1762.CrossRefGoogle ScholarPubMed
Peng, T. Q., Sun, G., & Wu, Y. (2017). Interplay between public attention and public emotion toward multiple social issues on Twitter. PLoS ONE, 12, e0167896.CrossRefGoogle ScholarPubMed
Perrin, J. S., Herve, P. Y., Leonard, G., et al. (2008). Growth of white matter in the adolescent brain: Role of testosterone and androgen receptor. Journal of Neuroscience, 28, 9519–9524.CrossRefGoogle ScholarPubMed
Pesaresi, M., Soon-Shiong, R., French, L., Kaplan, D. R., Miller, F. D., & Paus, T. (2015). Axon diameter and axonal transport: In vivo and in vitro effects of androgens. Neuroimage, 115, 191–201.CrossRefGoogle ScholarPubMed
Pike, G. B. (1996). Pulsed magnetization transfer contrast in gradient echo imaging: A two-pool analytic description of signal response. Magnetic Resonance in Medicine, 36, 95–103.CrossRefGoogle ScholarPubMed
Pipitone, J., Park, M. T., Winterburn, J., et al. (2014). Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage, 101, 494–12.CrossRefGoogle ScholarPubMed
Rakic, P. (1974). Neurons in rhesus monkey visual cortex: Systematic relation between time of origin and eventual disposition. Science, 183, 425–427.CrossRefGoogle ScholarPubMed
Rakic, P. (1988). Specification of cerebral cortical areas. Science, 241, 170–176.CrossRefGoogle ScholarPubMed
Rakic, P. (1995). A small step for the cell, a giant leap for mankind: A hypothesis of neocortical expansion during evolution. Trends in Neuroscience, 18, 383–388.CrossRefGoogle ScholarPubMed
Rakic, P. (2009). Evolution of the neocortex: A perspective from developmental biology. Nature Reviews Neuroscience, 10, 724–735.CrossRefGoogle ScholarPubMed
Roberts, T. P., & Mikulis, D. (2007). Neuro MR: Principles. Journal of Magnetic Resonance Imaging, 26, 823–837.CrossRefGoogle ScholarPubMed
Ruiz Jdel, C., Quackenboss, J. J., & Tulve, N. S. (2016). Contributions of a child’s built, natural, and social environments to their general cognitive ability: A systematic scoping review. PLoS ONE, 11, e0147741.CrossRefGoogle ScholarPubMed
Sandler, I., Wolchik, S. A., Cruden, G., et al. (2014). Overview of meta-analyses of the prevention of mental health, substance use, and conduct problems. Annual Review of Clinical Psychology, 10, 243–273.CrossRefGoogle ScholarPubMed
Satterthwaite, T. D., Elliott, M. A., Ruparel, K., et al. (2014). Neuroimaging of the Philadelphia neurodevelopmental cohort. Neuroimage, 86, 544–553.CrossRefGoogle ScholarPubMed
Schmierer, K., Scaravilli, F., Altmann, D. R., Barker, G. J., & Miller, D. H. (2004). Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Annals of Neurology, 56, 407–415.CrossRefGoogle ScholarPubMed
Schmierer, K., Wheeler-Kingshott, C. A., Tozer, D. J., et al. (2008). Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation. Magnetic Resonance in Medicine, 59, 268–277.CrossRefGoogle ScholarPubMed
Schumann, G., Loth, E., Banaschewski, T., et al. (2010). The IMAGEN study: Reinforcement-related behaviour in normal brain function and psychopathology. Molecular Psychiatry, 15, 1128–1139.CrossRefGoogle ScholarPubMed
Schüz, A., & Braitenberg, V. (2002). The Human Cortical White Matter: Quantitative Aspects of Cortico-Cortical Long-Range Connectivity. London: Taylor & Francis.CrossRefGoogle Scholar
Selemon, L. D., Ceritoglu, C., Ratnanather, J. T., et al. (2013). Distinct abnormalities of the primate prefrontal cortex caused by ionizing radiation in early or midgestation. The Journal of Comparative Neurology, 521, 1040–1053.Google Scholar
Shin, J., French, L., Xu, T., et al. (2018). Cell-specific gene-expression profiles and cortical thickness in the human brain. Cerebral Cortex, 28, 3267–3277.CrossRefGoogle ScholarPubMed
Sisk, C. L., & Foster, D. L. (2004). The neural basis of puberty and adolescence. Nature Neuroscience, 7, 1040–1047.CrossRefGoogle ScholarPubMed
Sloper, J. J. (1973). An electron microscopic study of the neurons of the primate motor and somatic sensory cortices. Journal of Neurocytology, 2, 351–359.Google Scholar
Sloper, J. J., Hiorns, R. W., & Powell, T. P. (1979). A qualitative and quantitative electron microscopic study of the neurons in the primate motor and somatic sensory cortices. Philosophical Transactions of the Royal Society of London B: Biological Science, 285, 141–171.Google ScholarPubMed
Toro, R., Perron, M., Pike, B., et al. (2008). Brain size and folding of the human cerebral cortex. Cerebral Cortex, 18, 2352–2357.Google Scholar
Walhovd, K. B., Fjell, A. M., Giedd, J., Dale, A. M., & Brown, T. T. (2017). Through thick and thin: A need to reconcile contradictory results on trajectories in human cortical development. Cerebral Cortex, 27, 1472–1481.Google Scholar
Whitaker, K. J., Vertes, P. E., Romero-Garcia, R., et al. (2016). Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proceedings of the National Academy of Sciences (USA), 113, 9105–9110.CrossRefGoogle ScholarPubMed
White, T., El Marroun, H., Nijs, I., et al. (2013). Pediatric population-based neuroimaging and the Generation R Study: The intersection of developmental neuroscience and epidemiology. European Journal of Epidemiology, 28, 99–111.Google Scholar
Zecevic, N., Chen, Y., & Filipovic, R. (2005). Contributions of cortical subventricular zone to the development of the human cerebral cortex. The Journal of Comparative Neurology, 491, 109–122.CrossRefGoogle Scholar
Zeisel, A., Munoz-Manchado, A. B., Codeluppi, S., et al. (2015). Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347, 1138–1142.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×