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Functional connectivity (FC) as derived from fMRI has emerged as a pivotaltool in elucidating the intricacies of various psychiatric disorders anddelineating the neural pathways that underpin cognitive and behavioral dynamicsinherent to the human brain. While Graph Neural Networks (GNNs) offer astructured approach to represent neuroimaging data, they are limited by theirneed for a predefined graph structure to depict associations between brainregions, a detail not solely provided by FCs. To bridge this gap, we introducethe Gated Graph Transformer (GGT) framework, designed to predict cognitivemetrics based on FCs. Empirical validation on the PhiladelphiaNeurodevelopmental Cohort (PNC) underscores the superior predictive prowess ofour model, further accentuating its potential in identifying pivotal neuralconnectivities that correlate with human cognitive processes.

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies