Deep learning models for brain-behaviour associations

Deep learning approaches based on Canonical Correlation Analysis (CCA), such as Deep Canonical Correlation Analysis (DCCA, Andrew et al. 2013) and Deep Canonically Correlated Autoencoders (DCCAE, Wang et al. 2015), are machine learning techniques that can learn linear and non-linear transformations of two views of the same samples/subjects in a common/latent space, in which the learnt representations are highly correlated.

This project will aim at employing these transformations to create mappings between multivariate brain patterns and behaviour/clinical patterns by using large samples of healthy individuals as well as individuals with mental health disorders. The developed generative models will then be used to test specific hypothesis of brain-behaviour associations related to mental health disorders. For example, given specific clinical/cognitive profiles, the model will be able to generate the corresponding brain patterns and vice-versa. This innovative modelling approach is expected to shed light on the underlying mechanisms of brain-behaviour association.

Further applications of the developed framework will include utilising the latent space to perform patient stratification according to mental health disorders, and using the generative models to synthesize missing data to aid predictive studies.

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.