Brain Atlases and Neuroanatomic Imaging

  • Allan MacKenzie-Graham
  • Jyl Boline
  • Arthur W. Toga
Part of the Methods in Molecular Biology™ book series (MIMB, volume 401)


Quantifying the effect of a genetic manipulation or disease is a complicated process in a population of animals. Probabilistic brain atlases can capture population variability and be used to quantify those variations in anatomy as measured by structural imaging. Minimum deformation atlases (MDAs), a subclass of probabilistic atlases, are intensity-based averages of a collection of scans in a common space unbiased by selection of a single target image. Here, we describe a method for generating an MDA from a set of magnetic resonance microscopy images. First, the images are segmented to remove any non-brain tissue and bias field corrected to remove field inhomogeneities. The corrected images are then linearly aligned to a representative scan, the geometric mean of all the transformations is calculated, and a minimum deformation target (MDT) is produced by averaging the volumes in this new space. The brains are then non-linearly aligned to the MDT to produce the MDA. Finally, the images are linearly aligned to the MDA using a full-affine transformation to spatially and intensity normalize them, removing global differences in size, shape, and position but retaining anatomically significant differences.

Key Words

Mouse MRI probabilistic variability spatial normalization. 


  1. 1.
    Franklin, K.B.J. and G. Paxinos, The Mouse Brain in Stereotaxic Coordinates. 1997, San Diego, CA: Academic Press. p. xxii, (186) of plates.Google Scholar
  2. 2.
    Paxinos, G. and K.B.J. Franklin, The Mouse Brain in Stereotaxic Coordinates, 2nd ed. 2001, San Diego, CA: Academic Press. p. xxv, (264) of plates.Google Scholar
  3. 3.
    Hof, P.R., et al., Comparative Cytoarchitectonic Atlas of the C57BL 6 and 129 Sv Mouse Brains. 2000, Amsterdam and New York: Elsevier.Google Scholar
  4. 4.
    Valverde, F., Golgi Atlas of the Postnatal Mouse Brain. 1998, Vienna, Austria: Springer-Verlag.Google Scholar
  5. 5.
    Toga, A.W. and P.M. Thompson, Multimodal brain atlases, in Advances in Biomedical Image Databases, S. Wong, Editor. 1998, Dordrecht, the Netherlands: Kluwer Academic Press. p. 53–88.Google Scholar
  6. 6.
    Mazziotta, J.C., et al., A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM). Neuroimage, 1995. 2(2): 89–101.CrossRefPubMedGoogle Scholar
  7. 7.
    Thompson, P.M., et al., Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. J Comput Assist Tomogr, 1997. 21(4): 567–581.CrossRefPubMedGoogle Scholar
  8. 8.
    Thompson, P.M., C. Schwartz, and A.W. Toga, High-resolution random mesh algorithms for creating a probabilistic 3D surface atlas of the human brain. Neuroimage, 1996. 3(1): 19–34.CrossRefPubMedGoogle Scholar
  9. 9.
    Thompson, P.M. and A.W. Toga, Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic brain atlas based on random vector field transformations. Med Image Anal, 1997. 1(4): 271–294.CrossRefPubMedGoogle Scholar
  10. 10.
    Ashburner, J. and K. Friston, Multimodal image coregistration and partitioning–a unified framework. Neuroimage, 1997. 6(3): 209–217.CrossRefPubMedGoogle Scholar
  11. 11.
    Kochunov, P., et al., Regional spatial normalization: toward an optimal target. J Comput Assist Tomogr, 2001. 25(5): 805–816.CrossRefPubMedGoogle Scholar
  12. 12.
    Kovacevic, N., et al., A three-dimensional MRI atlas of the mouse brain with estimates of the average and variability. Cereb Cortex, 2005. 15(5): 639–645.CrossRefPubMedGoogle Scholar
  13. 13.
    Ma, Y., et al., A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Neuroscience, 2005. 135(4): 1203–1215.CrossRefPubMedGoogle Scholar
  14. 14.
    Rex, D.E., J.Q. Ma, and A.W. Toga, The LONI pipeline processing environment. Neuroimage, 2003. 19(3): 1033–1048.CrossRefPubMedGoogle Scholar
  15. 15.
    Shattuck, D.W. and R.M. Leahy, BrainSuite: an automated cortical surface identification tool. Med Image Anal, 2002. 6(2): 129–142.CrossRefPubMedGoogle Scholar
  16. 16.
    Sled, J.G., A.P. Zijdenbos, and A.C. Evans, A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging, 1998. 17(1): 87–97.CrossRefPubMedGoogle Scholar
  17. 17.
    Mackenzie-Graham, A., et al., Cerebellar Cortical Atrophy in Experimental Autoimmune Encephalomyelitis, 2006.Google Scholar
  18. 18.
    Neu, S.C., D.J. Valentino, and A.W. Toga, The LONI Debabeler: a mediator for neuroimaging software. Neuroimage, 2005. 24(4): 1170–1179.CrossRefPubMedGoogle Scholar
  19. 19.
    Woods, R.P., et al., Automated image registration: I. General methods and intrasubject, intramodality validation. J Comput Assist Tomogr, 1998. 22(1): 139–152.CrossRefPubMedGoogle Scholar
  20. 20.
    Woods, R.P., et al., Automated image registration: II. Intersubject validation of linear and nonlinear models. J Comput Assist Tomogr, 1998. 22(1): 153–165.CrossRefPubMedGoogle Scholar

Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  • Allan MacKenzie-Graham
    • 1
  • Jyl Boline
    • 1
  • Arthur W. Toga
    • 1
  1. 1.Laboratory of Neuro Imaging, Department of NeurologyUniversity of CaliforniaLos Angeles

Personalised recommendations