Colato, Elisa;
(2022)
Network-based magnetic resonance imaging measures for clinical trials in multiple sclerosis.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
My work, presented in this thesis, aimed to define MRI markers to be used in clinical trials for identifying participants most likely to worsen, monitoring disease progression, and assessing treatment effects. With my first study (Chapter 3), I identified from T1-weighted sequences data-driven patterns of grey matter covarying volumes that predicted physical and cognitive disability in a large cohort of participants with secondary progressive multiple sclerosis. Moreover, some of the identified components were better correlated with concurrent disability, and some better predicted disability progression than conventionally used MRI measures (i.e. regional and whole-brain volume). Therefore, with this study, I identified clinically relevant structural patterns that could be used in clinical trials to stratify participants that are most likely to progress. With my second study (Chapter 4), I expanded on the first project by investigating the involvement of microstructural WM and GM damage as prognostic markers of clinical disability and cognitive dysfunctions in multiple sclerosis. I found networks of microstructural changes predictive of clinical progression and cognitive worsening. Moreover, this was the first study to use standardised T1-weighted/T2-weighted measures of white and grey matter to identify patterns of covarying microstructural damage changes and use them to predict clinical and cognitive worsening in multiple sclerosis. Finally, because these measures were obtained from MRI sequences routinely acquired in clinical trials, they hold promises to be broadly used in future clinical trials. With the third and last study (Chapter 5), I have developed a new paradigm to obtain longitudinal individual-level network-based measures of grey matter regional volume changes by applying independent component analysis (ICA) and a self-supervised machine learning model. The identified networks were clinically relevant as they discriminated among multiple sclerosis phenotypes, explained clinical disability, and showed treatment effect. Moreover, while the ICA needs to be run on the whole cohort, the approach I developed allows retrieving network-based measures at the individual level without re-estimating model parameters on the whole population when applied to new data (e.g. participants and time-points). These measures could be used in future clinical trials to complement conventional MRI measures and open the possibility of estimating network measures prospectively and at the individual level.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Network-based magnetic resonance imaging measures for clinical trials in multiple sclerosis |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | 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 UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10153029 |
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