The WHO considers autism as a priority due to its high-frequency, early onset, and life-long impact on quality of life. Discovering mechanisms that predict which autistic person develops mental health problems (prognostic biomarker), and which treatment may benefit individuals (predictive biomarker) requires candidate biomarkers derived using the most robust predictive methods. This PhD leverages data from the multi-centre Longitudinal European Autism Project (Loth 2016, 2017), including clinical, cognitive, neuroimaging, environmental and genetic data from ~700 individuals (430 autistic) aged 6-35 years.
Computational modelling distils multivariate neuroimaging into parsimonious parameters. One approach is to summarise whole-brain connectomes using graph theory (Váša 2022). Another approach is dynamical causal modelling (DCM) to map directed effective connections (Shine 2022). These complex methods provide mechanistic understanding of anatomy and neurodynamics in health and disease. However, their added prognostic value over simpler imaging or cognitive measures is unclear.
Aims & Yearly Objectives:
Compare predictive utility of graph-theory and DCMs to regional anatomy, activity and cognition for predicting disease course of autism spectrum disorders. Identify individual connectivity profiles and test their relation to concurrent or future development of mental health problems (controlling for environmental risk and genetics).
Yr1: Constructing connectomes and DCMs from the EU-AIMS LEAP project.
Yr2: Application of machine learning algorithms to define candidate biomarkers.
Yr3/4: Test added value of connectivity modeling for prognostics in longitudinal dataset.
This project bridges cognitive neuroscience and computational modelling, and embeds the student within a vibrant research community with leading experts on autism and neuroimaging.