  Functional connectivity (FC) as derived from fMRI has emerged as a pivotal
tool in elucidating the intricacies of various psychiatric disorders and
delineating the neural pathways that underpin cognitive and behavioral dynamics
inherent to the human brain. While Graph Neural Networks (GNNs) offer a
structured approach to represent neuroimaging data, they are limited by their
need for a predefined graph structure to depict associations between brain
regions, a detail not solely provided by FCs. To bridge this gap, we introduce
the Gated Graph Transformer (GGT) framework, designed to predict cognitive
metrics based on FCs. Empirical validation on the Philadelphia
Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of
our model, further accentuating its potential in identifying pivotal neural
connectivities that correlate with human cognitive processes.
