  The MRI-derived brain network serves as a pivotal instrument in elucidating
both the structural and functional aspects of the brain, encompassing the
ramifications of diseases and developmental processes. However, prevailing
methodologies, often focusing on synchronous BOLD signals from functional MRI
(fMRI), may not capture directional influences among brain regions and rarely
tackle temporal functional dynamics. In this study, we first construct the
brain-effective network via the dynamic causal model. Subsequently, we
introduce an interpretable graph learning framework termed Spatio-Temporal
Embedding ODE (STE-ODE). This framework incorporates specifically designed
directed node embedding layers, aiming at capturing the dynamic interplay
between structural and effective networks via an ordinary differential equation
(ODE) model, which characterizes spatial-temporal brain dynamics. Our framework
is validated on several clinical phenotype 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.
