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.

翻译：功能连接（FC）源自功能磁共振成像（fMRI），已成为阐明精神疾病复杂性并描绘人脑认知与行为动力学神经通路的关键工具。图神经网络（GNNs）为神经影像数据提供了结构化表示方法，但其局限在于需要预定义的图结构来刻画脑区之间的关联——这一细节无法仅由功能连接提供。为弥补这一不足，我们提出了门控图Transformer（GGT）框架，旨在基于功能连接预测认知指标。在费城神经发育队列（PNC）上的实证验证凸显了该模型的卓越预测能力，进一步彰显了其在识别与人类认知过程相关的关键神经连接方面的潜力。