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


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.

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Figure 1: Growth patterns in the developing human brain detected at ages 3–15 years.
Figure 2: Mapping dynamic patterns of brain development: four-dimensional growth maps.
Figure 3: Patterns of cerebral growth.
Figure 4: Detecting three-dimensional patterns of deep nuclear tissue loss.


  1. 1

    Fox, N. C., Freeborough, P. A. & Rossor, M. N. Visualisation and quantification of rates of atrophy in Alzheimer's disease. Lancet 348, 94– 97 (1996).

    CAS  Article  Google Scholar 

  2. 2

    Giedd, J. N. et al. Quantitative magnetic resonance imaging of human brain development: ages 4–18. Cereb. Cortex 6, 551– 560 (1996).

    CAS  Article  Google Scholar 

  3. 3

    Yakovlev, P. I. & Lecours, A. R. in Regional Development of the Brain in Early Life (ed. Minkowski, A.) 3– 70 (Davis, Philadelphia, 1967).

    Google Scholar 

  4. 4

    Sowell, E. R., Thompson, P. M., Holmes, C. J., Jernigan, T. L. & Toga, A. W. In vivo evidence for post-adolescent brain maturation frontal and striatal regions. Nature Neurosci. 2, 859– 861 (1999).

    CAS  Article  Google Scholar 

  5. 5

    Chugani, H. T., Phelps, M. E. & Mazziotta, J. C. Positron emission tomography study of human brain functional development. Ann. Neurol. 22, 487–497 (1987).

    CAS  Article  Google Scholar 

  6. 6

    Grimshaw, G. M., Adelstein, A., Bryden, M. P. & MacKinnon, G. E. First-language acquisition in adolescence: evidence for a critical period for verbal language development. Brain Lang. 63, 237–255 (1998).

    CAS  Article  Google Scholar 

  7. 7

    Thompson, P. M. et al. Cortical variability and asymmetry in normal aging and Alzheimer's disease. Cereb. Cortex 8, 492– 509 (1998).

    CAS  Article  Google Scholar 

  8. 8

    Zijdenbos, A. P. & Dawant, B. M. Brain segmentation and white matter lesion detection in MR images. Crit. Rev. Biomed. Eng. 22, 401–465 ( 1994).

    CAS  PubMed  Google Scholar 

  9. 9

    Woods, R. P., Cherry, S. R. & Mazziotta, J. C. Rapid automated algorithm for aligning and reslicing PET images. J. Comp. Assist. Tomogr. 16, 620–633 (1992).

    CAS  Article  Google Scholar 

  10. 10

    Freeborough, P. A., Woods, R. P. & Fox, N. C. Accurate registration of serial 3D MR brain images and its application to visualizing change in neurodegenerative disorders. J. Comp. Assist. Tomogr. 20, 1012– 1022 (1996).

    CAS  Article  Google Scholar 

  11. 11

    MacDonald, D., Avis, D. & Evans, A. C. in Proc. SPIE Conf. Visualization in Biomedical Computing (ed. Robb, R. A.) 2359, 160–169 (1994).

    Google Scholar 

  12. 12

    Thompson, P. M. & Toga, A. W. A surface-based technique for warping 3-dimensional images of the brain. IEEE Trans. Med. Imag. 15, 471–489 ( 1996).

    Article  Google Scholar 

  13. 13

    Thompson, P. M. & Toga, A. W. Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic brain atlas based on random vector field transformations. Med. Image Anal. 1, 271–294 ( 1997).

    CAS  Article  Google Scholar 

  14. 14

    Thompson, P. M. & Toga, A. W. in Brain Warping (ed. Toga, A. W.) 311–336 (Academic, San Diego, 1998).

    Google Scholar 

  15. 15

    Thompson, P. M., Schwartz, C., Lin, R. T., Khan, A. A. & Toga, A. W. 3D statistical analysis of sulcal variability in the human brain. J. Neurosci. 16, 4261– 4274 (1996).

    CAS  Article  Google Scholar 

  16. 16

    Thompson, P. M. et al. Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. J. Comp. Assist. Tomogr. 21, 567–581 (1997).

    CAS  Article  Google Scholar 

  17. 17

    Davatzikos, C. Spatial normalization of 3D brain images using deformable models. J. Comp. Assist. Tomogr. 20, 656–665 (1996).

    CAS  Article  Google Scholar 

  18. 18

    Miller, M. I. & Grenander, U. Computational anatomy: an emerging discipline. Q. Appl. Math. 56, 617– 694 (1998).

    MathSciNet  Article  Google Scholar 

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We thank E. Sowell, M. Mega and J. Mazziotta for their advice and support. P.M.T. was supported by the Howard Hughes Medical Institute, the US Information Agency, and the US–UK Fulbright Commission. Additional research support was provided by a Human Brain Project grant to the International Consortium for Brain Mapping, funded jointly by NIMH and NIDA, by National Institutes of Health intramural funding (J.N.G.), and by the National Library of Medicine, National Science Foundation, and the NCRR.

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Correspondence to Arthur W. Toga.

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Thompson, P., Giedd, J., Woods, R. et al. Growth patterns in the developing brain detected by using continuum mechanical tensor maps. Nature 404, 190–193 (2000).

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