How does myelination the development of the fatty sheath around brain neurons influence cognition during adolescence?

However, there are certain aspects of brain structure and function that do level off during development. For example, the number of neurons peaks even before birth; some 100 billion are formed during just the first 5 months of gestation. (Recent evidence suggests that new neurons are produced throughout life, though far less rapidly, and probably in numbers sufficient only to replace those that gradually die off.)

In spite of the great number of neurons present at birth, brain size itself increases more gradually. This growth is largely due to changes in individual neurons, which are structured much like trees. Thus, each brain cell begins as a tiny sapling and only gradually sprouts its hundreds of long, branching dendrites. Brain growth (measured as either weight or volume) is largely due to the growth of these dendrites, which serve as the receiving point for synaptic input from other neurons.

Another way of measuring brain development is to look at the speed of neural processing. A newborn’s brain works considerably more slowly than an adult’s, transmitting information some sixteen times less efficiently. The speed of neural processing increases dramatically during infancy and childhood, reaching its maximum at about age 15. Most of this increase is due to the gradual myelination of nerve cell axons (the long “wires” that connect one neuron to another neuron’s dendrites.) Myelin is a very dense, fatty substance that insulates axons much like the plastic sheath on a power cable, increasing the speed of electrical transmission and preventing cross-talk between adjacent nerve fibers. Myelination (the coating or covering of axons with myelin) begins around birth and is most rapid in the first 2 years but continues perhaps as late as 30 years of age.

Synaptic development is a more complicated issue. Synapses are the connecting points between the axon of one neuron and the dendrite of another. While information travels down the length of a single neuron as an electrical signal, it is transmitted across the synapse through the release of tiny packets of chemicals or, neurotransmitters. On the receiving (post-synaptic) side, special receptors for neurotransmitters change the chemical signal into an electrical signal, repeating the process in this next neuron in the chain. The number of synapses in the cerebral cortex peaks within the first few years of life, but then declines by about one third between early childhood and adolescence.

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, 247248.CrossRefGoogle Scholar

Aboitiz, F., Scheibel, A. B., Fisher, R. S., & Zaidel, E. (1992). Fiber composition of the human corpus callosum. Brain Research, 598, 143153.CrossRefGoogle ScholarPubMed

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, 532541.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, 108118.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, 112.CrossRefGoogle Scholar

Bystron, I., Blakemore, C., Rakic, P., & Molnar, Z. (2006). The first neurons of the human cerebral cortex. Nature Neuroscience, 9, 880886.CrossRefGoogle ScholarPubMed

Davey-Smith, G., & Hemani, G. (2014). Mendelian randomization: Genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 23, R89R98.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, 19211933.CrossRefGoogle 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, 1105011055.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, 1622.CrossRefGoogle ScholarPubMed

Giedd, J. N., Blumenthal, J., Jeffries, N. O., et al. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2, 861863.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, 4349.CrossRefGoogle ScholarPubMed

Gilmore, J. H., Knickmeyer, R. C., & Gao, W. (2018). Imaging structural and functional brain development in early childhood. Nature Reviews Neuroscience, 19, 123137.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, 81748179.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, 391399.CrossRefGoogle ScholarPubMed

Henkelman, R. M., Stanisz, G. J., & Graham, S. J. (2001). Magnetization transfer in MRI: A review. NMR in Biomedicine, 14, 5764.CrossRefGoogle ScholarPubMed

Herculano-Houzel, S. (2009). The human brain in numbers: A linearly scaled-up primate brain. Frontiers in Human Neuroscience, 3, 31.CrossRefGoogle ScholarPubMed

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), S25S33.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, 255262.CrossRefGoogle ScholarPubMed

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, 31513162.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, 721728.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, 15461555.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, 11491154.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, 1217612182.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, 578584.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, 521529.CrossRefGoogle ScholarPubMed

Kum, H. C., Krishnamurthy, A., Machanavajjhala, A., & Ahalt, S. (2014). Social genome: Putting big data to work for population informatics. Computer 47, 5663.CrossRefGoogle Scholar

Lerch, J. P., Van der Kouwe, A. J., Raznahan, A., et al. (2017). Studying neuroanatomy using MRI. Nature Neuroscience, 20, 314326.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, 539547.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, 335342.CrossRefGoogle ScholarPubMed

McGowan, J. C. (1999). The physical basis of magnetization transfer imaging. Neurology, 53, S3S7.Google ScholarPubMed

Moore, M. M., & Chung, T. (2017). Review of key concepts in magnetic resonance physics. Pediatric Radiology, 47, 497506.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, 10831094.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, 33513362.CrossRefGoogle ScholarPubMed

Paus, T. (2005). Mapping brain maturation and cognitive development during adolescence. Trends in Cognitive Science, 9, 6068.CrossRefGoogle ScholarPubMed

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, 255266.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, 502518.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 Scholar

Pelvig, D. P., Pakkenberg, H., Stark, A. K., & Pakkenberg, B. (2008). Neocortical glial cell numbers in human brains. Neurobiology of Aging, 29, 17541762.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, 95199524.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, 191201.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, 95103.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, 425427.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, 383388.CrossRefGoogle ScholarPubMed

Rakic, P. (2009). Evolution of the neocortex: A perspective from developmental biology. Nature Reviews Neuroscience, 10, 724735.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, 243273.CrossRefGoogle ScholarPubMed

Satterthwaite, T. D., Elliott, M. A., Ruparel, K., et al. (2014). Neuroimaging of the Philadelphia neurodevelopmental cohort. Neuroimage, 86, 544553.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, 407415.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, 268277.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, 11281139.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, 10401053.CrossRefGoogle ScholarPubMed

Shin, J., French, L., Xu, T., et al. (2018). Cell-specific gene-expression profiles and cortical thickness in the human brain. Cerebral Cortex, 28, 32673277.CrossRefGoogle ScholarPubMed

Sisk, C. L., & Foster, D. L. (2004). The neural basis of puberty and adolescence. Nature Neuroscience, 7, 10401047.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, 351359.CrossRefGoogle ScholarPubMed

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, 141171.Google ScholarPubMed

Toro, R., Perron, M., Pike, B., et al. (2008). Brain size and folding of the human cerebral cortex. Cerebral Cortex, 18, 23522357.CrossRefGoogle ScholarPubMed

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, 14721481.Google ScholarPubMed

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, 91059110.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, 99111.CrossRefGoogle ScholarPubMed

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, 109122.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, 11381142.CrossRefGoogle ScholarPubMed

How does myelination influence cognition during adolescence?

These studies have also shown that teens have less white matter (myelin) in the frontal lobes compared to adults, and that myelin in the frontal lobes increases throughout adolescence. With more myelin comes the growth of important neurocircuitry, allowing for better flow of information between brain regions.

How does myelination affect brain development?

Myelination for Language Myelination allows more rapid transmission of neural information along neural fibers and is particularly critical in a cerebral nervous system dependent on several long axon connections between hemispheres, lobes, and cortical and subcortical structures.

What happens to the myelin sheath during adolescence and early adulthood?

Although myelination begins early in life and continues into adulthood, its production escalates notably during adolescence (25), thereby speeding information flow across distant regions and magnifying their impact (26).

What happens to the neurons in your brain during adolescence?

Summary: Researchers have found that adolescence is a time of remodeling in the prefrontal cortex, a brain structure dedicated to higher functions such as planning and social behaviors.