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Edge Weights and Network Properties in Multiple Sclerosis

  • Elizabeth PowellEmail author
  • Ferran Prados
  • Declan Chard
  • Ahmed Toosy
  • Jonathan D. Clayden
  • Claudia Gandini A. M. Wheeler-Kingshott
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Graph theory is able to provide quantitative parameters that describe structural and functional characteristics of human brain networks. Comparisons between subject populations have demonstrated topological disruptions in many neurological disorders; however interpreting network parameters and assessing the extent of the damage is challenging. The abstraction of brain connectivity to a set of nodes and edges in a graph is non-trivial, and factors from image acquisition, post-processing and network construction can all influence derived network parameters. We consider here the impact of edge weighting schemes in a comparative analysis of structural brain networks, using healthy control and relapsing-remitting multiple sclerosis subjects as test groups. We demonstrate that the choice of edge property can substantially affect inferences of network disruptions in disease, ranging from ‘primarily intact connectivity’ to ‘complete disruption’. Although study design should predominantly dictate the choice of edge weight, it is important to consider how study outcomes may be affected.

Keywords

Graph theory Network Edge weight Graph property Connectivity Permutation Multiple sclerosis 

Notes

Acknowledgements

The NMR unit where this work was performed is supported by grants from the Multiple Sclerosis Society of Great Britain and Northern Ireland, Philips Healthcare, and supported by the UCL/UCLH NIHR (National Institute for Health Research) BRC (Biomedical Research Centre).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Elizabeth Powell
    • 1
    • 2
    Email author
  • Ferran Prados
    • 2
    • 3
  • Declan Chard
    • 2
    • 4
  • Ahmed Toosy
    • 2
  • Jonathan D. Clayden
    • 5
  • Claudia Gandini A. M. Wheeler-Kingshott
    • 2
    • 6
    • 7
  1. 1.Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
  2. 2.Faculty of Brain SciencesQueen Square MS Centre, UCL Institute of Neurology, University College LondonLondonUK
  3. 3.Department of Medical Physics and BioengineeringCentre for Medical Image Computing, University College LondonLondonUK
  4. 4.National Institute for Health Research Biomedical Research Centre, University College London HospitalsLondonUK
  5. 5.Developmental Imaging and Biophysics SectionGreat Ormond Street Institute of Child Health, University College LondonLondonUK
  6. 6.Department of Brain and Behavioural SciencesUniversity of PaviaPaviaItaly
  7. 7.Brain MRI 3T Center, IRCCS Mondino FoundationPaviaItaly

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