PhD Studentship: Using human movement analysis to track disease progression in neurogenetic disorde
In this project we will use human movement analysis techniques to track development of movement abnormalities in people carrying mutations which cause neurological disease, but who have not yet developed the neurological condition. In all of the common neurological disorders a proportion are caused by single gene mutations. Individuals carrying mutations in genes causing neurological disease represent a unique opportunity to understand the earliest phases of neurological disorders.
This project will use 2 genomic disorders to study the earliest phases of 2 common neurological conditions. The 22q11 deletion syndrome is one of the most common genetic disorders in humans (c. 1/2000, equivalent to cystic fibrosis), it is associated with a 20 fold increased risk of Parkinson’s disease. Dr Alisdair McNeill has recruited a cohort of 70 adults with 22q11 deletion syndrome and identified a subset who have potential early signs of Parkinson’s disease (impaired olfaction, subtle motor signs). Genetic cerebellar ataxia is a rare cause of movement abnormalities in humans. Dr Alisdair McNeill has recruited a cohort of individuals with genetic ataxia (n=20) and a small group of individuals with genetic mutations and no symptoms (n=6). Pilot data indicates obvious gait abnormalities in the manifest ataxia group and subtle gait impairment in the presymptomatic group. In this project the student will use state of the art human movement analysis techniques on these cohorts to identify progressive gait deterioration as a marker of disease progression. The Optogait 5m system uses 2 rows of floor mounted sensors which the participant walks between. The participant’s footfalls are recorded. The Optogait 5m system then reports on 25 spatial (e.g. step width) and temporal (e.g. step time, velocity) variables. At the same time the participant wears inertial sensors on the trunk and limbs (Opals system) to provide data on truncal stability during walking. Data is extracted using custom Matlab scripts and analysed using standard statistical approaches. The gait analysis will be performed at baseline, 12 months and 24 months. Gait data will be examined to identify any deterioration in gait parameters which might suggest neurological deterioration. Machine learning techniques (e.g. Support Vector Machine, Artificial Neural Network) will be applied to the data to identify gait abnormalities not identifiable by standard statistical approaches. This project will help to identify human movement analysis as a potential biomarker of disease progression for clinical use in neurological practice.
The Faculty Scholarships for Medicine, Dentistry & Health cover fees and stipend at Home/EU level. Overseas students may apply but will need to fund the fee differential between Home and Overseas rate from another source.
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