Edge-boosted graph learning for functional brain connectivity analysis
Journal:
arXiv
Published Date:
Apr 21, 2025
Abstract
Predicting disease states from functional brain connectivity is critical for
the early diagnosis of severe neurodegenerative diseases such as Alzheimer's
Disease and Parkinson's Disease. Existing studies commonly employ Graph Neural
Networks (GNNs) to infer clinical diagnoses from node-based brain connectivity
matrices generated through node-to-node similarities of regionally averaged
fMRI signals. However, recent neuroscience studies found that such node-based
connectivity does not accurately capture ``functional connections" within the
brain. This paper proposes a novel approach to brain network analysis that
emphasizes edge functional connectivity (eFC), shifting the focus to inter-edge
relationships. Additionally, we introduce a co-embedding technique to integrate
edge functional connections effectively. Experimental results on the ADNI and
PPMI datasets demonstrate that our method significantly outperforms
state-of-the-art GNN methods in classifying functional brain networks.
