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Normalisation of Neonatal Brain Network Measures Using Stochastic Approaches
Markus Schirmer1, Gareth Ball1, Serena J. Counsell1, A. David Edwards1, Daniel Rueckert2, Joseph V. Hajnal1, and Paul Aljabar1
1Division of Imaging Sciences & Biomedical Engineering, King’s College London, UK
markus.schirmer@kcl.ac.uk
2BioMedIA Group, Dept. of Computing, Imperial College London, UK
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
LNCS 8149, p. 574 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013