Deep Learning of Cortical Surface Features Using Graph-Convolution Predicts Neonatal Brain Age and Neurodevelopmental Outcome

Abstract

We investigated the ability of graph convolutional network (GCN) that takes into account the mesh topology as a sparse graph to predict brain age for preterm neonates using cortical surface morphometrics, i.e. cortical thickness and sulcal depth. Compared to machine learning and deep learning methods that did not use the surface topological information, the GCN better predicted the ages for preterm neonates with none/mild perinatal brain injuries (NMI). We then tested the GCN trained using NMI brains to predict the age of neonates with severe brain injuries (SI). Results also displayed good accuracy ( $\text{MAE}=1.43$ weeks), while the analysis of the interaction term (true age × group) showed that the slope of the predicted brain age relative to the true age for the SI group was significantly less steep than the NMI group ( $\mathrm{p} ), indicating that SI can decelerate early postnatal growth. To understand regional contributions to age prediction, we applied GCNs separately to the vertices within each cortical parcellation. The middle cingulate cortex that is known to be one of the thickest cortical regions in the neonatal period showed the best accuracy in age prediction ( $\text{MAE}=1.24$ weeks). Furthermore, we found that the regional brain ages computed using GCN models in several frontal cortices significantly correlated with cognitive abilities at 3 years of age. Furthermore, the brain predicted age in part of the superior temporal cortex, which is the auditory and language processing locus, was related to language functional scores at 3 years. Our results demonstrate the potential of the GCN models for predicting brain age as well as localizing brain regions contributing to the prediction of age and future cognitive outcome.