  MRI-based modeling of brain networks has been widely used to understand
functional and structural interactions and connections among brain regions, and
factors that affect them, such as brain development and disease. Graph mining
on brain networks may facilitate the discovery of novel biomarkers for clinical
phenotypes and neurodegenerative diseases. Since brain networks derived from
functional and structural MRI describe the brain topology from different
perspectives, exploring a representation that combines these cross-modality
brain networks is non-trivial. Most current studies aim to extract a fused
representation of the two types of brain network by projecting the structural
network to the functional counterpart. Since the functional network is dynamic
and the structural network is static, mapping a static object to a dynamic
object is suboptimal. However, mapping in the opposite direction is not
feasible due to the non-negativity requirement of current graph learning
techniques. Here, we propose a novel graph learning framework, known as Deep
Signed Brain Networks (DSBN), with a signed graph encoder that, from an
opposite perspective, learns the cross-modality representations by projecting
the functional network to the structural counterpart. We validate our framework
on clinical phenotype and neurodegenerative disease prediction tasks using two
independent, publicly available datasets (HCP and OASIS). The experimental
results clearly demonstrate the advantages of our model compared to several
state-of-the-art methods.
