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.

翻译：从功能磁共振成像(fMRI)数据中提取的功能连接(FC)网络上训练的深度神经网络，因数据可用性的提高和包括图神经网络(GNN)在内的模型架构的进步而日益受到关注。近期关于GNN应用于FC的研究表明，利用FC的时变特性可显著提升模型预测的准确性和可解释性。然而，获取高质量fMRI数据及其表型标签的高昂成本，对其在现实场景中的应用构成障碍，使得以监督方式朴素训练的模型在少量数据上可能面临性能不足或缺乏泛化能力的问题。此外，当前多数面向GNN的自监督学习(SSL)方法采用对比策略，这种策略在扰动图结构时容易丢失适当的语义信息，或无法同时利用空间和时间信息。针对这些挑战，我们提出了一种生成式SSL方法，专门用于有效利用动态FC中的时空信息。我们在大规模（>50,000）fMRI数据集上的实验结果表明，我们的方法能够学习到有价值的表征，并在针对下游任务进行微调时，能够构建出准确且鲁棒的模型。