Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
This work addresses the challenge of modeling temporal dynamics in brain connectomes for neuroscience and medical applications, though it is incremental as it adapts existing Graph Transformers to a specific domain.
The authors tackled the problem of learning representations from dynamic brain functional connectivity networks, which fluctuate over time, by proposing a Graph Transformer-based method that incorporates position, structure, and time information. Their method outperformed other baselines in gender classification and age regression tasks using over 50,000 fMRI samples, the largest dataset in such studies.
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.