Systems-based machine learning approach to understanding clinical, genetic, and pathophysiological heterogeneity in Parkinson's dementia

Brief description of the context of the research including potential impact

Parkinson's disease (PD) is highly heterogeneous. Although PD is the 2nd most common neurodegenerative disease, we have a limited understanding of the clinical and neuroanatomical sequence of its progression. Half of diagnosed patients also develop dementia within 10 years and these patients typically have a worse prognosis. Additionally, no robust biomarkers to predict risk of developing Parkinson's, nor to track disease progression have been identified.

The availability of large multimodal datasets, such as the Parkinson's Progression Markers Initiative and the UCL Vision in Parkinson's study, presents an opportunity to deploy the latest developments in data-driven disease progression modelling and subtyping technology to deepen our understanding of PD. Subsequently, this research may inform clinical trial recruitment for people with PD at higher risk of developing dementia.

Aims and Objectives

The overarching objectives is to characterise the clinical, genetic, and pathophysiological heterogeneity in Parkinson's dementia by combining available longitudinal data from multimodal neuroimaging, retinal imaging, and clinical assessments (both novel and traditional). Specifically:
1. Identify PD progression subtypes in terms of the sequence, timing, and severity of observable abnormality in multimodal datasets using data-driven disease progression modelling.
2. Identify medical imaging signatures of PD progression by repeating subtype experiments using only neuroimaging data. Likewise, for novel tests of vision, with a view to understanding progression in PD dementia.
3. Identify the minimum set of features necessary for personalised medicine in PD by quantifying feature importance for each subtype
Thus, to link PD dementia subtypes to clinical symptoms and pathophysiology.

Novelty of Research Methodology

The main novelty of this research is the ability to extract longitudinal information from cross-sectional data, in order to characterize and determine subtypes of PD. Specifically, the Subtype and Stage Inference algorithm is used, which is a machine-learning technique that can extract phenotypes with specific temporal progression patterns. Joint estimation of characteristics of PD (sequence, timing, and symptom severity) will be performed using a single unified model, which tackles the relative sparseness of the data, but takes advantage of the depth. The Vision in Parkinson's disease study also contains novel neuroimaging techniques to detect early changes in brain connectivity, such as fixel based analyses of white matter integrity and quantitative susceptibility mapping.

Alignment to EPSRC's strategies and research areas

This project aligns to several of the EPSRC's strategies and research areas. However, the main alignment is with "Disruptive technologies for sensing and analysis", as a novel method is being applied to further understand PD and determine any potential biomarkers specific to subtypes. Depending on the progress of the project, this may potentially translate to a clinical support system (and thus may "transform community care"). Specifically, the project aims to inform clinical trial recruitment and thus may lead to the "development of future therapies" and "treatment optimisation". This project works closely with clinicians (the secondary supervisor) to inform project design.

With regards to the EPSRC's strategic focus within Medical imaging, this project tackles two areas of high priority:
- Enabling earlier and more effective diagnosis of physical and mental health conditions, to inform treatment planning
- Integration of existing and additional information from clinical data/images (e.g. via machine learning and/or mathematical science techniques)

Any companies or collaborators involved

There are no signed companies or collaborators in this project.

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.