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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)

Abstract

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.

Keywords

neonatal MRI diffusion connectivity network analysis 

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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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