Neuroimaging and neurochemical ageing biomarkers for optimising prognosis in motor neurone disease

1) Brief description of the context of the research including potential impact
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with no known cure. The time between disease onset and the end stages of disease vary widely from patient to patient. Patients with ALS endure rapid widespread brain tissue loss and this process typically progresses quicker in elder people. ALS is therefore thought to interact with the ageing process, which by itself usually causes gradual loss of brain tissue. It may be the case that individuals with larger amounts of age-related tissue loss may have a more fatal (shorter survival time) prognosis if they develop neurodegenerative diseases like ALS. To this end, machine learning algorithms in tandem with magnetic resonance imaging (MRI) data, have been used to predict disease progression in neurodegenerative diseases. This is done through the 'brain age' index, which is a measure that is informative for brain health. Additionally, neurofilaments data taken from blood samples, are informative for brain atrophy. In health, neurofilaments typically reside in the cytoplasm of neurons, and are released into the blood after neurons die. As individuals age and normal brain tissue loss occurs the levels of neurofilaments in blood also increases. Both MRI and neurofilaments data have been useful for predicting outcomes in ALS but have not previously been used together. The present research aims to combine data from both modalities in-order to develop techniques which aid ALS prognostication. This will be done through the application of computational methods, with a particular focus on machine learning. These statistical/machine learning models will be sensitive to accelerated age-related brain tissue loss as well as increased neurofilaments in the blood. Ultimately, such models will be able to recognise the rate at which ALS will progress in an individual patient. This will increase the speed and cost effectiveness of clinical trials for ALS interventions, as well as enabling targeted treatments.

2) Aims and Objectives
-The specific objectives are to:
- Develop multimodal (MRI and blood) machine learning and other statistical models that are sensitive to accelerated age-related changes to the brain.
- Use these models to correctly classify whether ALS will progress quickly or slowly within an individual.
- Apply these approaches to aid in the development of targeted treatments and more efficient clinical trials for ALS drug treatments.

3) Novelty of Research Methodology
Our research methodology is novel primarily due to the multi-modal nature of the machine learning/statistical models that will be developed. MRI and blood samples data have independently shown promise in predicting ALS prognosis, but their combination should provide even greater power to predictive models. Also, the use of 'brain age' as well as, machine learning techniques in aiding prognostication of neurodegenerative diseases is a recent development. Therefore, this project will be able to unlock more of the potential these techniques have already shown.


4) Alignment to EPSRC's strategies and research areas
This project is aligned with the EPSRC's healthcare technology strategy. Within that, the project is aligned with the medical imaging, clinical technologies and analytic science research areas.

5) Any companies or collaborators involved
N/A

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.