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Developing methods to accurately and objectively measure neurological disorders such as Parkinson’s Disease and Progressive Supranuclear Palsy.

A group of people in the NeuroMetrology Lab group stood outside

Our approach

Most medical conditions can be rapidly and objectively quantified using standard equipment. However, the 'gold standard' measure of many brain diseases is still a clinical rating scale, a system of points assigned by an observer based on their impression of the person's condition. Such scales show significant inter-observer variability, and they are also nonlinear, limiting the statistical analyses that can be applied to them.

The NeuroMetrology laboratory led by Professor Chrystalina Antoniades uses precise measurements of abnormalities of movement and its control in order to quantify neurodegenerative diseases. Eye movements have proved to be a particularly rich source of information because they can be evaluated quickly and reliably with equipment that is portable and therefore usable in a clinic setting. Gait and balance abnormalities are key features of several neurodegenerative diseases, and can be measured using wearable sensors, either in clinic or remotely. We are also developing methods of measurement using both manual and cognitive tests, and have shown that these can be sensitive enough to detect dysfunction in Parkinson's disease even in its very early stages.

 RESEARCH STUDIES

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Selected publications

Identification of motor progression in Parkinson’s disease using wearable sensors and machine learning

Journal article

Sotirakis C. et al, (2023), npj Parkinson's Disease, 9

ntiparkinsonian medication masks motor signal progression in de novo patients

Journal article

Brzezicki MA. et al, (2023), Heliyon, 9, e16415 - e16415

Deep Brain Stimulation and Levodopa Affect Gait Variability in Parkinson Disease Differently

Journal article

Su ZH. et al, (2023), Neuromodulation: Technology at the Neural Interface, 26, 382 - 393

Longitudinal Monitoring of Progressive Supranuclear Palsy using Body‐Worn Movement Sensors

Journal article

Sotirakis C. et al, (2022), Movement Disorders, 37, 2263 - 2271

Oculomotor deficits in Parkinson's disease: Increasing sensitivity using multivariate approaches

Journal article

Bredemeyer O. et al, (2022), Frontiers in Digital Health, 4