Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data. However, existing GNN methods assume brain graphs are static over time and the graph adjacency matrix is known prior to model training. These assumptions contradict evidence that brain graphs are time-varying with a connectivity structure that depends on the choice of functional connectivity measure. Incorrectly representing fMRI data with noisy brain graphs can adversely affect GNN performance. To address this, we propose DynDepNet, a novel method for learning the optimal time-varying dependency structure of fMRI data induced by downstream prediction tasks. Experiments on real-world fMRI datasets, for the task of sex classification, demonstrate that DynDepNet achieves state-of-the-art results, outperforming the best baseline in terms of accuracy by approximately 8 and 6 percentage points, respectively. Furthermore, analysis of the learned dynamic graphs reveals prediction-related brain regions consistent with existing neuroscience literature.

翻译：图神经网络（GNN）在处理功能磁共振成像（fMRI）数据构建的脑图谱表示学习任务中已展现出显著优势。然而，现有GNN方法假设脑图谱具有静态时空特性，且图邻接矩阵在模型训练前已知。这些假设与脑图谱具有时变特性且其连接结构依赖于功能连接度量选择的证据相矛盾。若使用含噪声的脑图谱错误表示fMRI数据，将直接影响GNN性能。为此，我们提出DynDepNet——一种通过下游预测任务驱动学习fMRI数据最优时变依赖结构的新方法。在真实fMRI数据集上开展的性别分类实验表明，DynDepNet取得了最先进的分类性能，其准确率分别比最优基线方法高出约8和6个百分点。此外，对学习到的动态图结构进行分析，发现了与已有神经科学文献一致的与预测任务相关的脑区。