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Title: Defining disease progression in ALS : a novel analytic approach using existing clinical and imaging datasets
Author: Gabel, Matthew Christopher Edward
ISNI:       0000 0004 7427 0644
Awarding Body: University of Brighton
Current Institution: University of Brighton
Date of Award: 2017
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Background: A key aim of medical science is modelling patterns of disease progression; these patterns increase understanding of the disease, and help construct staging systems that assist diagnosis and treatment. Within Amyotrophic Lateral Sclerosis (ALS) disease progression modelling, there is a need to integrate clinical observation-based staging systems such as Roche et al. (2012), which suffer from low temporal resolution, with ‘unbiased’ staging of biomarkers. To this end, I have adapted and extended an EventBased Model (EBM) for ALS from previous work in Alzheimer’s disease (Fonteijn et al., 2012; Young et al., 2014). Unlike traditional models of disease progression, eventbased models do not rely on a priori staging of patients but extract the event ordering directly from the data, thus minimising subjective bias. In MR imaging, Fractional Anisotropy (FA) derived from diffusion tensor imaging is an obvious candidate to test the hypothesis that imaging events can be staged in the ALS-adapted EBM. Objectives: Using contemporary and historical ALS datasets comprised of diffusion MRI, clinical and neuropsychological data, I have adapted and extended a novel event-based model to analyse the likely ordering of these biomarkers in the progression of ALS. Materials and Methods The contemporary dataset was derived from a cross-sectional sample of 23 ALS patients and 23 matched controls (Broad et al., 2015). The two historical datasets were similarly derived from samples of i) 36 ALS patients and 22 matched controls, and ii) 28 ALS patients and 25 matched controls (Tsermentseli et al., 2015). The ALSspecific adaptations to the EBM were i) the fitting of Gaussian mixture models by constrained Expectation Maximisation, ii) the calculation of event probabilities from the cumulative distribution function to preserve the monotonicity of biomarker reading progression, and iii) accounting for the clearly delineated patient and control cohorts by performing Markov Chain Monte Carlo (MCMC) sampling on only the patient cohort. Finally, a fully Bayesian approach to Event-Based Modelling is demonstrated. Results: The most likely order of progression of imaging events showed that FA changes in the lower aspect of the corticospinal tracts (CSTs) occur at an early stage of disease evolution, with changes in the upper aspect occurring at a later stage. This result was found individually in all three datasets, as well as when combining them. Discussion: This proof-of-principle study shows that data-driven models of ALS progression are feasible, as well as demonstrating a fully Bayesian approach to Event-Based Modelling. The diffusion MRI event ordering results suggest very robustly that damage to the CSTs starts in the lower aspect. Nevertheless, a general important limitation must be discussed: The small sample size may have biased our results. I have tried to address this issue by assessing how the results varied across three separate datasets, both individually and combined. While the CST results were consistent across the entire process, results for other regions such as the corpus callosum were less constant, suggesting that the biomarker ordering in the wider population may diverge from this sequence. In order to generalise these results to the wider spectrum of ALS, future studies on larger datasets are warranted. Conclusion: These findings provide the first solely data-driven evidence supporting a directional hypothesis of motor neurone degeneration.
Supervisor: Cercignani, Mara Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available