Reading minds with Deep Learning: predicting behavioural states from functional imaging data

Aim of the PhD Project:

The goal is to:

Develop tools for spatio-temporal Deep Learning of brain function
For prediction of neuro-developmental outcome in vulnerable preterm babies,
And development of biomarkers sensitive to risk of ADHD and Autism
Project Description / Background:

Precision diagnosis of complex cognitive disorders, such as Autism and ADHD, is extremely challenging since such disorders are characterised by a highly heterogeneous range of cognitive and behavioural traits. Such traits are extremely difficult to characterise as they reflect subtle features of the spatio-temporal dynamics of brain activity.

Currently, the most popular technique for analysing resting-state functional imaging data is to perform spatial-ICA (independent component analysis [1]). This models the brain as a macroscale network, formed from a set of functionally specialised regions, each associated with a time course. Network connectivity is then inferred by estimating similarities between time courses using correlation measures [1].

Although matrix factorisation approaches such as ICA, have significantly improved our understanding of how brain function relates to behaviour, they smooth out vital sources of inter-subject variation. Specifically, ICA analyses look at the average properties of brain states over time, whereas, it has been shown that many behavioural measures are better predicted by dynamic measures [2]. Further, studies assume a single global average model of cortical organisation; however, there is growing evidence that this is not the case [3,4].

What is required are tools that can learn temporal and spatial features from the data without requirement for prior modelling or spatial normalisation of the data. This problem lends itself to deep learning; we therefore seek to take inspiration from recent works on spatio-temporal convolutional deep learning for natural image processing [5,6], cardiac imaging [7] and functional Magnetic Resonance Imaging (fMRI [8]), in order to classify pathological brain states, and support precision diagnosis of neuro-developmental disorders.

Given that studies of cognition require precision analysis of the brain's surface (or cortex, [9,10]), a key objective will be to extend models to geometric deep learning [11,12], which trains on surface manifolds, rather than 2D or 3D grids. Significant emphasis will also be placed on the development of interpretable models [6]. This will support clinical interpretation.

The most suitable candidate for this project will have programming expertise in Python, and experience in Deep Learning. Experience in working with spatio-temporal data sets or geometric deep learning would be a significant plus.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.