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.

基于功能连接和神经影像数据，引入了门控图神经转换器（GGT）框架来预测认知指标，通过在费城神经发育队列（PNC）上进行实证验证，强调了该模型在识别与人类认知过程相关的关键神经连接方面的潜力。