Sekar, Akila Ramamoorthy; (2025) Exploring Ocular Motor Biomarkers in Parkinson’s Disease and Atypical Parkinsonian Syndromes. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Parkinson’s disease and atypical Parkinsonian syndromes—including progressive supranuclear palsy, multiple system atrophy, corticobasal syndrome, and dementia with Lewy bodies—are rising in global prevalence due to an ageing population. These conditions are challenging to diagnose in the early stages, as they share over- lapping clinical symptoms and currently rely on subjective clinical assessments, costly imaging techniques, and laboratory tests. Moreover, these assessments often require input from specialists, whose availability may be limited, leading to delays in both diagnosis and the initiation of treatment. This thesis investigates the potential of eye movements as diagnostic biomarkers for Parkinson’s disease and atypical Parkinsonian syndromes. Utilizing a comprehensive ocular motor battery and advanced eye-tracking technologies, the first part of the thesis aims to develop a unique ocular motor profile capable of distin- guishing between Parkinson’s subtypes and atypical syndromes. Subsequent projects examine the effects of dopaminergic treatment in Parkinson’s disease and analyse longitudinal changes in eye movements to understand how these metrics evolve over time and how these changes are affected with short term and long term medication use. This approach seeks to establish the utility of eye movement measures in tracking disease progression. Additionally, studying the short-term effects of dopamine will inform future ocular motor studies by determining whether and how the impact of medication must be accounted for in experimental design and interpretation. Another study within this thesis explores saccadic adaptation in Parkinson’s disease and multiple system atrophy, offering key insights into motor learning deficits and neuroplasticity. Investigating such paradigms enhances our understanding of how neural connectivity and functionality are altered in specific pathologies. The final section of the thesis focuses on integrating machine learning, portable eye-tracking devices, and mobile applications into ocular motor research. Portable eye trackers offer a more accessible and scalable alternative to traditional equipment, enabling assessments in both clinical and home settings. When combined with machine learning algorithms, these tools can enhance diagnostic accuracy by detecting subtle, condition-specific eye movement patterns. Furthermore, the development of mobile and tablet-based applications facilitates large-scale, remote assessments—reducing barriers to specialist evaluations and allowing for continuous, real-time disease monitoring. By combining clinical neuroscience, artificial intelligence, and digital health, this thesis presents evidence for the use of eye movements in diagnosing Parkinson’s disease and atypical Parkinsonian syndromes. With continued advancements, eye-tracking technology has the potential to become a vital tool for early detection, diagnosis, disease monitoring, and possibly the development of personalized treatment strategies.

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