RT Journal Article
SR Electronic
T1 Population Graph GNNs for Brain Age Prediction
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.26.172171
DO 10.1101/2020.06.26.172171
A1 Kamilė Stankevičiūtė
A1 Tiago Azevedo
A1 Alexander Campbell
A1 Richard Bethlehem
A1 Pietro Liò
YR 2020
UL http://biorxiv.org/content/early/2020/07/04/2020.06.26.172171.abstract
AB 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 multimodal 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.Competing Interest StatementThe authors have declared no competing interest.