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Edge Weights and Network Properties in Multiple Sclerosis

Powell, E; Prados, F; Chard, D; Toosy, A; Clayden, JD; Wheeler-Kingshott, CGAM; (2019) Edge Weights and Network Properties in Multiple Sclerosis. In: Frangi, Alejandro and Alberola-Lopez, Carlos, (eds.) Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018). (pp. pp. 281-291). Springer: Cham, Switzerland. Green open access

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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.

Type: Proceedings paper
Title: Edge Weights and Network Properties in Multiple Sclerosis
Event: 21st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018), 16-20 Sept 2018, Granada, Spain
ISBN: 978-3-030-05830-2
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-05831-9_22
Publisher version: https://doi.org/10.1007/978-3-030-05831-9_22
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Graph theory, Network, Edge weight, Graph property, Connectivity, Permutation, Multiple sclerosis
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10079674
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