Deep neural networks trained on Functional Connectivity (FC) networks
extracted from functional Magnetic Resonance Imaging (fMRI) data have gained
popularity due to the increasing availability of data and advances in model
architectures, including Graph Neural Network (GNN). Recent research on the
application of GNN to FC suggests that exploiting the time-varying properties
of the FC could significantly improve the accuracy and interpretability of the
model prediction. However, the high cost of acquiring high-quality fMRI data
and corresponding phenotypic labels poses a hurdle to their application in
real-world settings, such that a model na\"ively trained in a supervised
fashion can suffer from insufficient performance or a lack of generalization on
a small number of data. In addition, most Self-Supervised Learning (SSL)
approaches for GNNs to date adopt a contrastive strategy, which tends to lose
appropriate semantic information when the graph structure is perturbed or does
not leverage both spatial and temporal information simultaneously. In light of
these challenges, we propose a generative SSL approach that is tailored to
effectively harness spatio-temporal information within dynamic FC. Our
empirical results, experimented with large-scale (>50,000) fMRI datasets,
demonstrate that our approach learns valuable representations and enables the
construction of accurate and robust models when fine-tuned for downstream
tasks.

利用动态功能连接图中的时空信息，我们提出了一种生成式自监督学习方法，通过大规模 fMRI 数据集的实证结果表明，我们的方法在下游任务的微调中能够学习到有价值的表示，并构建出准确且稳健的模型。