Large-scale Graph Representation Learning of Dynamic Brain Connectome
  with Transformers
Byung-Hoon Kim Jungwon Choi EungGu Yun Kyungsang Kim Xiang Li Juho Lee
Graph Transformers have recently been successful in various graph
representation learning tasks, providing a number of advantages over
message-passing Graph Neural Networks. Utilizing Graph Transformers for
learning the representation of the brain functional connectivity network is
also gaining interest. However, studies to date have underlooked the temporal
dynamics of functional connectivity, which fluctuates over time. Here, we
propose a method for learning the representation of dynamic functional
connectivity with Graph Transformers. Specifically, we define the connectome
embedding, which holds the position, structure, and time information of the
functional connectivity graph, and use Transformers to learn its representation
across time. We perform experiments with over 50,000 resting-state fMRI samples
obtained from three datasets, which is the largest number of fMRI data used in
studies by far. The experimental results show that our proposed method
outperforms other competitive baselines in gender classification and age
regression tasks based on the functional connectivity extracted from the fMRI
data.