Normalisation of Neonatal Brain Network Measures Using Stochastic Approaches

  • Markus Schirmer
  • Gareth Ball
  • Serena J. Counsell
  • A. David Edwards
  • Daniel Rueckert
  • Joseph V. Hajnal
  • Paul Aljabar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)


Diffusion tensor imaging, tractography and the subsequent derivation of network measures are becoming an established approach in the exploration of brain connectivity. However, no gold standard exists in respect to how the brain should be parcellated and therefore a variety of atlas- and random-based parcellation methods are used. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences necessitates the use of normalisation schemes to enable meaningful intra- and inter-subject comparisons. This work proposes methods for normalising brain network measures using random graphs. We show that the normalised measures are locally stable over distinct random parcellations of the same subject and, applying it to a neonatal serial diffusion MRI data set, we demonstrate their potential in characterising changes in brain connectivity during early development.


neonatal MRI diffusion connectivity network analysis 


  1. 1.
    Sporns, O.: The human connectome: a complex network. Ann. N.Y. Acad. Sci. 1224, 109–125 (2011)CrossRefGoogle Scholar
  2. 2.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)CrossRefGoogle Scholar
  3. 3.
    Supekar, et al.: Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput. Biol. 4(6), 1–11 (2008)CrossRefGoogle Scholar
  4. 4.
    Alexander, et al.: Diffusion tensor imaging of the brain. Neurotherapeutics 4(3), 316–329 (2007)CrossRefGoogle Scholar
  5. 5.
    Delobel-Ayoub, et al.: Behavioral problems and cognitive performance at 5 years of age after very preterm birth: the EPIPAGE Study. Pediatrics 123(6), 1485–1492 (2009)CrossRefGoogle Scholar
  6. 6.
    Sporns, O., Tononi, G., Kötter, R.: The human connectome: A structural description of the human brain. PLoS Comput. Biol. 1(4), 245–251 (2005)CrossRefGoogle Scholar
  7. 7.
    Hagmann, et al: Mapping the structural core of human cerebral cortex. PLoS Biol. 6(7), 1479–1493 (2008)CrossRefGoogle Scholar
  8. 8.
    Vall, et al.: The influence of preterm birth on the developing thalamocortical connectome. Cortex, 1–11 (2012)Google Scholar
  9. 9.
    Bridson, R.: Fast poisson disk sampling in arbitrary dimensions. In: ACM SIGGRAPH, vol. 2007 (2007)Google Scholar
  10. 10.
    Behrens, et al.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34(1), 144–155 (2007)CrossRefGoogle Scholar
  11. 11.
    Robinson, et al.: Identifying population differences in whole-brain structural networks: a machine learning approach. NeuroImage 50(3), 910–919 (2010)CrossRefGoogle Scholar
  12. 12.
    Zalesky, et al.: Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50(3), 970–983 (2010)CrossRefGoogle Scholar
  13. 13.
    Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Science 296(5569), 910–913 (2002)CrossRefGoogle Scholar
  14. 14.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  15. 15.
    Fagiolo, G.: Clustering in complex directed networks. Phys. Rev. E 76(2), 026107 (2007)Google Scholar
  16. 16.
    Van Wijk, et al.: Comparing brain networks of different size and connectivity density using graph theory. PloS one 5(10), 13701 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Markus Schirmer
    • 1
  • Gareth Ball
    • 1
  • Serena J. Counsell
    • 1
  • A. David Edwards
    • 1
  • Daniel Rueckert
    • 2
  • Joseph V. Hajnal
    • 1
  • Paul Aljabar
    • 1
  1. 1.Division of Imaging Sciences & Biomedical EngineeringKing’s College LondonUK
  2. 2.BioMedIA Group, Dept. of ComputingImperial College LondonUK

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