On the Limits of Applying Graph Transformers for Brain Connectome Classification

Jose Miguel Lara Rangel, Clare Elizabeth Heinbaugh

Track: long paper (up to 4 pages)

Keywords: Graph Deep Learning, Graph Transformers, Connectomes, Synthetic Dataset, Static Connectome Classification

TL;DR: This work explores sparse graph transformers, mainly Exphormer, for brain connectome classification with NeuroGraph datasets, finding no clear gains over traditional GNNs and questioning the relevance of graph structures in these datasets.

Abstract:

Brain connectomes offer detailed maps of neural connections within the brain. Recent studies have proposed novel connectome graph datasets and attempted to improve connectome classification by using graph deep learning. With recent advances demonstrating transformers’ ability to model intricate relationships and outperform in various domains, this work explores their performance on the novel NeuroGraph benchmark datasets and synthetic variants derived from probabilistically removing edges to simulate noisy data. Our findings suggest that graph transformers offer no major advantage over traditional GNNs on this dataset. Furthermore, both traditional and transformer GNN models maintain accuracy even with all edges removed, suggesting that the dataset’s graph structures may not significantly impact predictions. We propose further assessing NeuroGraph as a brain connectome benchmark, emphasizing the need for well-curated datasets and improved preprocessing strategies to obtain meaningful edge connections.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 21