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Growth patterns in the developing brain detected by using continuum mechanical tensor maps

Author

Listed:
  • Paul M. Thompson

    (Laboratory of Neuro Imaging, UCLA School of Medicine)

  • Jay N. Giedd

    (Child Psychiatry Branch, National Institute of Mental Health, NIH)

  • Roger P. Woods

    (Laboratory of Neuro Imaging, UCLA School of Medicine)

  • David MacDonald

    (Montreal Neurological Institute, McGill University)

  • Alan C. Evans

    (Montreal Neurological Institute, McGill University)

  • Arthur W. Toga

    (Laboratory of Neuro Imaging, UCLA School of Medicine)

Abstract

The dynamic nature of growth and degenerative disease processes requires the design of sensitive strategies to detect, track and quantify structural change in the brain in its full spatial and temporal complexity1. Although volumes of brain substructures are known to change during development2, detailed maps of these dynamic growth processes have been unavailable. Here we report the creation of spatially complex, four-dimensional quantitative maps of growth patterns in the developing human brain, detected using a tensor mapping strategy with greater spatial detail and sensitivity than previously obtainable. By repeatedly scanning children (aged 3–15 years) across time spans of up to four years, a rostro-caudal wave of growth was detected at the corpus callosum, a fibre system that relays information between brain hemispheres. Peak growth rates, in fibres innervating association and language cortices, were attenuated after puberty, and contrasted sharply with a severe, spatially localized loss of subcortical grey matter. Conversely, at ages 3–6 years, the fastest growth rates occurred in frontal networks that regulate the planning of new actions. Local rates, profiles, and principal directions of growth were visualized in each individual child.

Suggested Citation

  • Paul M. Thompson & Jay N. Giedd & Roger P. Woods & David MacDonald & Alan C. Evans & Arthur W. Toga, 2000. "Growth patterns in the developing brain detected by using continuum mechanical tensor maps," Nature, Nature, vol. 404(6774), pages 190-193, March.
  • Handle: RePEc:nat:nature:v:404:y:2000:i:6774:d:10.1038_35004593
    DOI: 10.1038/35004593
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    Cited by:

    1. Da-dong Guo & Jian-feng Wu & Yuan-yuan Hu & Wei Sun & Tai-liang Lv & Wen-jun Jiang & Hui Wu & Xing-rong Wang & Jost B Jonas & Hong-sheng Bi, 2016. "Stereoacuity and Related Factors: The Shandong Children Eye Study," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-13, July.
    2. Jo Mhairi Hale, 2017. "Cognitive Disparities: The Impact of the Great Depression and Cumulative Inequality on Later-Life Cognitive Function," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2125-2158, December.
    3. Ashvin Ahuja & Thitima Chucherd & Kobsak Pootrakool, 2006. "Human Capital Policy: Building a Competitive Workforce for 21st Century Thailand," Working Papers 2006-04, Monetary Policy Group, Bank of Thailand.
    4. Hank Johnston, 2019. "The Elephant in the Room: Youth, Cognition, and Student Groups in Mass Social Movements," Societies, MDPI, vol. 9(3), pages 1-19, August.
    5. Eleni Mitsea & Athanasios Drigas & Charalabos Skianis, 2022. "ICTs and Speed Learning in Special Education: High-Consciousness Training Strategies for High-Capacity Learners through Metacognition Lens," Technium Social Sciences Journal, Technium Science, vol. 27(1), pages 230-252, January.

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