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Parcellation-Independent Multi-Scale Framework for Brain Network Analysis

  • M. D. Schirmer
  • G. Ball
  • S. J. Counsell
  • A. D. Edwards
  • D. Rueckert
  • J. V. Hajnal
  • P. Aljabar
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Structural brain connectivity can be characterised by studies employing diffusion MR, tractography and the derivation of network measures. However, in some subject populations, such as neonates, the lack of a generally accepted paradigm for how the brain should be segmented or parcellated leads to the application of a variety of atlas- and random-based parcellation methods. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences has yet to be resolved, in order to enable more meaningful intra- and inter-subject comparisons. This work proposes a parcellation-independent multi-scale analysis of commonly used network measures to describe changes in the brain. As an illustration, we apply our framework to a neonatal serial diffusion MRI data set and show its potential in characterising developmental changes. Furthermore, we use the measures provided by the framework to investigate the inter-dependence between network measures and apply an hierarchical clustering algorithm to determine a subset of measures for characterising the brain.

Keywords

Betweenness Centrality Brain Network Network Measure Brain Connectivity High Betweenness Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Medical Research Council (MRC) Centre for Transplantation, King’s College London, UK—MRC grant no. MR/J006742/1.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • M. D. Schirmer
    • 1
    • 2
  • G. Ball
    • 1
    • 2
  • S. J. Counsell
    • 1
    • 2
  • A. D. Edwards
    • 1
    • 2
  • D. Rueckert
    • 2
    • 3
  • J. V. Hajnal
    • 1
    • 2
  • P. Aljabar
    • 1
    • 2
  1. 1.Division of Imaging Sciences & Biomedical EngineeringKing’s College London, St. Thomas’ HospitalLondonUK
  2. 2.Centre for the Developing BrainKing’s College London, St. Thomas’ HospitalLondonUK
  3. 3.BioMedIA Group, Department of ComputingImperial College LondonLondonUK

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