  One of the central problems in neuroscience is understanding how brain
structure relates to function. Naively one can relate the direct connections of
white matter fiber tracts between brain regions of interest (ROIs) to the
increased co-activation in the same pair of ROIs, but the link between
structural and functional connectomes (SCs and FCs) has proven to be much more
complex. To learn a realistic generative model characterizing population
variation in SCs, FCs, and the SC-FC coupling, we develop a graph auto-encoder
that we refer to as Staf-GATE. We trained Staf-GATE with data from the Human
Connectome Project (HCP) and show state-of-the-art performance in predicting FC
and joint generation of SC and FC. In addition, as a crucial component of the
proposed approach, we provide a masking-based algorithm to extract
interpretable inferences about SC-FC coupling. Our interpretation methods
identified important SC subnetworks for FC coupling and relating SC and FC with
sex.
