Insights on Using Graph Neural Networks for Sulcal Graphs Predictive Models

Alexis Imbert, Benoit Gaüzère, Sylvain Takerkart, Guillaume Auzias, Paul Honeine

Abstract: Graph representations have emerged as a powerful tool in medical imaging to analyze complex structures such as the brain. In particular, studying the geometry of the brain surface provides valuable insights into individual characteristics. To model the brain as a graph, sulcal graphs are derived from MRI data by assigning a node to each fold of the cortical surface and connecting these nodes based on their adjacency. This approach encodes the geometry of the brain surface, making sulcal graphs a relevant data representation for predictive models. In this study, we explore the effectiveness of Graph Neural Networks (GNNs) to extract meaningful information from these graph representations, using sex classification as a pretext task. Our experiments reveal that the structural information captured by the graph is largely driven by the 3D coordinates of the nodes, raising questions about the added value of graph connectivity in these representations. Additionally, we find that conventional Multi-Layer Perceptron (MLP) models achieve comparable or superior performance to GNNs, suggesting that graph structure may not provide significant additional discriminative power in this specific task. These findings highlight the challenges of defining optimal GNN architectures for sulcal graphs and motivate further investigations into alternative representations and learning paradigms.

External IDs:dblp:conf/gbrpr/ImbertGTAH25