Lopez, Seymour Mark;
(2025)
Machine learning-based investigation of disease progression of structural changes in the brain in people with epilepsy and its association with treatment response of anti-epileptic medications.
Masters thesis (M.Phil), UCL (University College London).
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Abstract
Epilepsy is a neurological disease associated with structural and functional changes in the brain, genetic mutations, as well as factors mediated through brain injury. While extensive research has documented structural changes in the brains of people with epilepsy, it remains unclear whether these changes follow a distinct pattern of progression from one brain region to another. Furthermore, existing antiepileptic drugs often show limited effectiveness in treating epilepsy, posing challenges in clinical management. Therefore, I investigate if there are image-derived subtypes of epilepsy based on the unique progression of structural changes in the brain and if staging these patients helps identify the response to antiepileptic drugs. This will help identify patients that require personalised or alternate treatments during the early diagnostic stages of epilepsy. Furthermore, it is unclear if there is a link between structural changes in the brain and the subtle facial asymmetry seen across a subset of people with epilepsy. Therefore, I aim to investigate this association, which will improve our understanding of the underlying mechanisms that cause facial asymmetry in people with epilepsy. This report is structured in 7 chapters, where: Chapter 1 which serves as the introduction, I outline the main motivations and objectives of the research conducted here; Chapter 2 explains the causes, diagnosis, treatments in epilepsy and subsequently outlines the motivations for my research, Chapter 3 gives an overview of machine learning techniques used across the research presented here; Chapter 4 investigates the association of structural changes in the brain with the asymmetry of facial features; In Chapter 5, using one of the world’s largest epilepsy cohorts, from the ENIGMA-Epilepsy working group, I estimate the progressive sequence of structural changes in the brain; Chapter 6 extends the work of Chapter 5 by investigating imaging-derived subtypes of epilepsy based on the unique progressive changes in the brain. Lastly, Chapter 7 outlines the conclusions and future work of the research reported here.
| Type: | Thesis (Masters) |
|---|---|
| Qualification: | M.Phil |
| Title: | Machine learning-based investigation of disease progression of structural changes in the brain in people with epilepsy and its association with treatment response of anti-epileptic medications |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2025. 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 UCL > Provost and Vice Provost Offices > UCL BEAMS 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/10208131 |
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