Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771806
Title: Investigating structural network disruption in multiple sclerosis
Author: Charalambous, Thalis
ISNI:       0000 0004 7659 9347
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Abstract:
Multiple sclerosis (MS) is an inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS). Conventional whole brain magnetic resonance imaging (MRI) measures do not sufficiently explain disability in MS. Network science provides a powerful approach to study brain organizational principles and in combination with graph theory has revealed fundamental connectivity patterns in neurological conditions including MS. The overarching aim of this thesis is to investigate structural network disruption in MS evaluating the potential of brain networks analyses as novel biomarkers in MS pathology. The results of this thesis add to the current scientific knowledge. In particular, by applying an optimised structural network reconstruction pipeline we demonstrated that network metrics explain disability better in MS over and above conventional non- network metrics. In addition, in the absence of any longitudinal network studies, we developed a longitudinal network pipeline which we then applied to our longitudinal data. These findings demonstrated for the first time that baseline structural network metrics are predictors of future deep grey matter atrophy and increased lesion load. Finally, we applied a data-driven network decomposition approach detecting progressive weakening of connections that is linked to the severity of MS subtypes suggesting that these techniques are sensitive to pathology. The results presented here highlight the potential of network-based approaches as complementary methods for disease biomarkers to better predict disease course and monitor treatment effects. We believe that these findings may provide a framework for future studies with the aim to bridge the gap between imaging and symptomatology.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.771806  DOI: Not available
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