Markers of disease progression

Developing ultrasound imaging as a potential diagnostic and non-invasive tool for MND

• Dr Emma Hodson-Tole, Prof Ian Loram, Dr Nicholas Costen and Dr Nicholas Combes
• Manchester Metropolitan University
• Grant awarded £82,295 (Biomedical project) over three years
• Start Date: October 2013
• Our Ref: 6294 Oct13

This project aims to determine whether ultrasound imaging could be developed as a non-invasive tool to aid the diagnosis of MND.

The group will develop computer-based pattern recognition techniques, to detect and measure the different involuntary contractions indicative of MND. They will then explore the sensitivity and accuracy of ultrasound as a diagnostic tool compared with conventional needle electrode examination.

Data will be collected from people with suspected MND who have been referred for assessment. The results of the consultant’s diagnosis and the ultrasound image analysis will be compared to provide evidence of whether their new approach could reliably identify cases of MND to an acceptable clinical standard.

Defining disease progression in MND using existing data

• Prof Mara Cercignani, Prof Nigel Leigh, Dr Andy Simmons, Prof Daniel Alexander, Prof Ammar Al-Chalabi
• University of Sussex
• £77,177 (PhD studentship) over three years
• Start date: October 2013
• Our Ref: 6293 Oct12

Although conventional brain magnetic resonance imaging (MRI) scans are often normal in people with MND, more sophisticated MRI techniques have shown changes in the structure of the brain. A limitation of even the most recent MRI techniques is that they provide a snapshot of the brain at a single moment in the course of the illness. Only a description of how these MRI changes evolve over time as the disease advances will tell us how the nerve cell damage is evolving area by area in relation to the individual's symptoms.

This studentship project will use MRI scans that have already been obtained from many studies at King's College London over the past 16 years. By applying new concepts in medical computing to this data this project will identify how MRI changes evolve in sequence, even using scans done on a single occasion. This will allow the integration of imaging information with clinical features, linking insights into the evolution of physical changes in the brain with clinical features to develop a new and objective method to 'stage' disease progression and to detect brain abnormalities early in the disease.