REVEAL V1.0
Research type
Research Study
Full title
Unravelling the Complexities of Movement Disorders: Advances Clinical and Instrumental Phenotyping
IRAS ID
345704
Contact name
Francesca Morgante
Contact email
Sponsor organisation
City St George’s University
Duration of Study in the UK
5 years, 0 months, 0 days
Research summary
Parkinson's Disease (PD), Action Tremor and Dystonia and are the most common movement disorders and are functionally disabling. Diagnosis and treatment of these conditions, rely on clinical features which are difficult to distinguish, even by expert clinicians. As a result, patients with these movement disorders are often misdiagnosed and fail treatment.
The primary purpose of this study is to understand PD, ET, and Dystonia using kinematic and neurophysiological techniques to capture elements of movement disorders that are often missed during clinical assessments. By recording the full range of motion and features that occur in different movement disorders, we hope to establish a range of behavioural characteristics to achieve deep phenotyping of movement disorders, aid in the delineation of diseases and the development of more effective treatments based on more specific disease target
We will apply multimodal kinematic and neurophysiological techniques to a large subset of PD, Action Tremor and Dystonia patients to achieve deep phenotyping of movement disorders. We will also investigate the relationship between identified motor features and key non-motor features of PD, ET and Dystonia, such as mental health, pain, sleep and assess the effects of different treatment modalities on these motor and non-motor features. We will then conduct cluster analysis and machine learning analysis using multimodal techniques, which could inform a new aetiopathologically-driven classification system. Conducting spectral and connectivity analysis of Electroencephalography (EEG), Electromyography (EMG) and local field potential data (LFP) will help to elucidate the distinctive underlying brain network activities. Moreover, machine learning and tailored decoding algorithms will be used to identify features in brain network activity relating to features of movement disorders in different patient subtypes. We will then leverage this information to understand how medical treatment and Deep Brain Stimulation (DBS) can be optimised using different physiomarkers.REC name
London - Queen Square Research Ethics Committee
REC reference
25/PR/0100
Date of REC Opinion
28 May 2025
REC opinion
Further Information Favourable Opinion