Many common neurological and neurodegenerative disorders, such as Alzheimer’s disease, dementia and multiple sclerosis, have been associated with abnormal patterns of apparent ageing of the brain. Discrepancies between the estimated brain age and the actual chronological age (brain age gaps) can be used to understand the biological pathways behind the ageing process, assess an individual’s risk for various brain disorders and identify new personalised treatment strategies. By flexibly integrating minimally preprocessed neuroimaging and non-imaging modalities into a population graph data structure, we train two types of graph neural network (GNN) architectures to predict brain age in a clinically relevant fashion as well as investigate their robustness to noisy inputs and graph sparsity. The population graph approach has the potential to learn from the entire cohort of healthy and affected subjects of both sexes at once, capturing a wide range of confounding effects and detecting variations in brain age trends between different sub-populations of subjects.

Teaser video |