Predicting autism spectrum phenotypes from neonatal brain connectivity

The goal of this project is to improve our understanding of brain development and outcome in babies at risk of autism spectrum disorders (ASD) by:

1.Training machine learning algorithms in adult populations to classify ASD from controls
2.Transferring techniques to state-of-the-art neonatal acquisitions
3. Interpreting algorithms to find altered connectivity patterns associated with outcome

Genetic and environmental risk factors acting from before and shortly after birth are associated with Autism Spectrum Disorders (ASD). However, ASD is highly diverse and not everyone at-risk goes on to develop the condition. Since early interventions work best, understanding underlying mechanisms that lead to ASD and establishing who is most likely to benefit from treatment is currently one of the most important neuroscientific challenges.

Histopathology and neuroimaging studies in children and adults with a diagnosis of ASD have shown disruptions to the organisation of neural systems. Recently, using a simple measure of functional connectivity (degree centrality), reproducible alterations in patients with ASD have been reported in independent datasets. Brain network analysis techniques have been used to show a reduction in global communication capacity in brain networks of three-year-old children with ASD. Despite these recent insights, even in young children, we still cannot easily untangle the causes of ASD from the secondary or compensatory effects of living with the condition. Thus, to really examine what makes a brain vulnerable to ASD, or indeed what might be protective, studies assessing structural and functional connectivity in the neonatal period in infants at risk of ASD together with information about childhood outcomes are needed.

Diffusion MRI (dMRI) and functional MRI (fMRI) have been widely employed to study structural and functional brain connectivity in the healthy and diseased neonatal brain. Recently, artificial Intelligence (AI), and machine learning in particular, has had a large impact on the field of medical image analysis and is opening new avenues of research. This project aims at taking advantage of such algorithms to address a key clinical challenge: link neonatal brain connectivity to typical and atypical neurodevelopmental trajectories and provide a means to subgroup ('stratify') the at-risk population earlier than ever before.

We already have a large and growing dataset acquired at KCL of typically-developing infants (N=~800); with state-of-the-art diffusion and functional neonatal MRI and neurocognitive follow-up. Importantly, we also scan neonates who are at greater risk of developing ASD traits than average because they have a strong family history of ASD or have had a significant perinatal exposure linked to ASD, recruited as part of the EU-AIMS Brain Imaging in Babies (BIBS) study. In addition, we will also have access to data from the KCL-lead EU-AIMS LEAP project, the largest multi-centre, multi-disciplinary observational study worldwide that aims to identify and validate stratification biomarkers for ASD. Data available includes 437 children and adults with ASD and 300 individuals with typical development. We will also take advantage of publicly available adult datasets such as ABIDE (including 539 ASD subjects and 573 typically developing controls).

In this project we will assess the predictive power of known reproducible alterations in ASD neuroimaging characteristics such as degree centrality in adults. Then we will transfer trained predictive algorithms to assess the capacity of neonatal brain connectivity markers to predict specific autistic phenotypes measured later in life, such as neurobehavioral testing and eye-tracking paradigms. Using newly developed interpretable machine learning methods we will determine if there are underlying patterns in neonatal brain network organisation and function which relate to childhood outcomes.

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.