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Normalisation of neonatal brain network measures using stochastic approaches

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention
Subtitle of host publicationProceedings of the 16th International Conference, Nagoya, Japan, September 22-26, 2013, Part 1
EditorsKensaku Mori, Ichiro Sakuma, Yoshinobu Sato, Christian Barillot, Nassir Navab
PublisherSpringer Berlin Heidelberg
Number of pages8
ISBN (Print)9783642408106
Publication statusPublished - 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013; - Japan, Nagoya, United Kingdom
Duration: 22 Sep 201326 Sep 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013;
CountryUnited Kingdom

King's Authors


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.

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