Graph Contrastive Learning for Connectome Classification
Journal:
arXiv
Published Date:
Feb 7, 2025
Abstract
With recent advancements in non-invasive techniques for measuring brain
activity, such as magnetic resonance imaging (MRI), the study of structural and
functional brain networks through graph signal processing (GSP) has gained
notable prominence. GSP stands as a key tool in unraveling the interplay
between the brain's function and structure, enabling the analysis of graphs
defined by the connections between regions of interest -- referred to as
connectomes in this context. Our work represents a further step in this
direction by exploring supervised contrastive learning methods within the realm
of graph representation learning. The main objective of this approach is to
generate subject-level (i.e., graph-level) vector representations that bring
together subjects sharing the same label while separating those with different
labels. These connectome embeddings are derived from a graph neural network
Encoder-Decoder architecture, which jointly considers structural and functional
connectivity. By leveraging data augmentation techniques, the proposed
framework achieves state-of-the-art performance in a gender classification task
using Human Connectome Project data. More broadly, our connectome-centric
methodological advances support the promising prospect of using GSP to discover
more about brain function, with potential impact to understanding heterogeneity
in the neurodegeneration for precision medicine and diagnosis.
