  Recent advances in neuroimaging along with algorithmic innovations in
statistical learning from network data offer a unique pathway to integrate
brain structure and function, and thus facilitate revealing some of the brain's
organizing principles at the system level. In this direction, we develop a
supervised graph representation learning framework to model the relationship
between brain structural connectivity (SC) and functional connectivity (FC) via
a graph encoder-decoder system, where the SC is used as input to predict
empirical FC. A trainable graph convolutional encoder captures direct and
indirect interactions between brain regions-of-interest that mimic actual
neural communications, as well as to integrate information from both the
structural network topology and nodal (i.e., region-specific) attributes. The
encoder learns node-level SC embeddings which are combined to generate (whole
brain) graph-level representations for reconstructing empirical FC networks.
The proposed end-to-end model utilizes a multi-objective loss function to
jointly reconstruct FC networks and learn discriminative graph representations
of the SC-to-FC mapping for downstream subject (i.e., graph-level)
classification. Comprehensive experiments demonstrate that the learnt
representations of said relationship capture valuable information from the
intrinsic properties of the subject's brain networks and lead to improved
accuracy in classifying a large population of heavy drinkers and non-drinkers
from the Human Connectome Project. Our work offers new insights on the
relationship between brain networks that support the promising prospect of
using graph representation learning to discover more about human brain activity
and function.
