deep learning

We examine the utility of graph neural networks for the purpose of learning cortical segmentations. We show that attention-based transformer networks significantly outperform conventional GCN and linear feed-forward variants for the purpose of generating accurate reproducible cortical maps.

In this analysis, we propose the use of a library of training brains to build a statistical model of the parcellated cortical surface to act as templates for mapping new MRI data.