Roguski, Amber

English

Over 70% of people with REM-Sleep Behaviour Disorder (RBD) will go on to develop one of the neurodegenerative alpha-synucleinopathies within 12 years, most commonly Parkinson’s disease (PD). Symptom-tracking remains the dominant clinical approach to RBD prognosis due to sub-optimal specificity/sensitivity of pathology-detection methods and inadequate prognostic biomarkers of disease progression. In particular, there have been limited efforts to explore combinations of biomarkers.The primary aim of this thesis was to identify novel prognostic biomarkers of RBD phenoconversion to PD. Frequentist statistical analysis was used to closely examine differences between groups and identify potential individual biomarkers, while machine learning was enlisted to develop a prognostic model to combine biomarkers and predict RBD prognosis.A multi-modal data collection approach was taken to deeply phenotype Control (n=19), isolated RBD (n=16) and early-stage PD (n=17) participants. Non-invasive phenotyping methods were chosen due to their applicability to the clinical setting. Data types ranging from motor assessments, participant reported outcome measures and electrophysiological data (EEG, ECG, EOG) were collected, to compare modalities. It was additionally hypothesised that electrophysiological investigations would be able to detect subtle signs of neurodegeneration that might not be captured by broad clinical assessments and questionnaires. The main results of this thesis include replication of previous findings in similar cohorts – for example, olfactory dysfunction, increased depressive symptoms and minimal changes to sleep macroarchitecture in RBD and PD groups. Novel analyses of heart rate variability, eye movements and REM microstates in the three groups are reported in this thesis, with some avenues laying promising foundations for future research. Novel analytical techniques were used for EEG microarchitecture analysis, and this is one of the first studies to report the use of machine learning classifiers for prognostic prediction in an RBD cohort.Generally, standardised clinical assessment measures proved to have the greatest prognostic power for distinguishing early-stage PD from Controls in this relatively small cohort. However, electrophysiological features may hold value for stratification of RBD phenoconverters according to disease outcome. The work detailed in this thesis provides direction for future, longitudinal studies in larger cohorts.<br/

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Developing a multimodal framework for stratification of severity and progression in isolated REM Sleep Behaviour Disorder

https://research-information.bris.ac.uk/files/364642855/Final_Copy_2023_05_09_Roguski_A_G_P_PhD.pdf

Developing a multimodal framework for stratification of severity and progression in isolated REM Sleep Behaviour Disorder

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