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Title: Computational modelling of imaging markers to support the diagnosis and monitoring of multiple sclerosis
Author: Eshaghi, Arman
ISNI:       0000 0004 7429 104X
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Multiple sclerosis is a leading cause of neurological disability in young adults which affects more than 2.5 million people worldwide. An important substrate of disability accrual is the loss of neurons and connections between them (neurodegeneration) which can be captured by serial brain imaging, especially in the cerebral grey matter. In this thesis in four separate subprojects, I aimed to assess the strength of imaging-derived grey matter volume as a biomarker in the diagnosis, predicting the evolution of multiple sclerosis, and developing a staging system to stratify patients. In total, I retrospectively studied 1701 subjects, of whom 1548 had longitudinal brain imaging data. I used advanced computational models to investigate cross-sectional and longitudinal datasets. In the cross-sectional study, I demonstrated that grey matter volumes could distinguish multiple sclerosis from another demyelinating disorder (neuromyelitis optica) with an accuracy of 74%. In longitudinal studies, I showed that over time the deep grey matter nuclei had the fastest rate of volume loss (up to 1.66% annual loss) across the brain regions in multiple sclerosis. The volume of the deep grey matter was the strongest predictor of disability progression. I found that multiple sclerosis affects different brain areas with a specific temporal order (or sequence) that starts with the deep grey matter nuclei, posterior cingulate cortex, precuneus, and cerebellum. Finally, with multivariate mechanistic and causal modelling, I showed that brain volume loss causes disability and cognitive worsening which can be delayed with a potential neuroprotective treatment (simvastatin). This work provides conclusive evidence that grey matter volume loss affects some brain regions more severely, can predict future disability progression, can be used as an outcome measure in phase II clinical trials, and causes clinical and cognitive worsening. This thesis also provides a subject staging system based on which patients can be scored during multiple sclerosis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available