Recently, graph neural networks (GNNs) have shown success at learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data. The majority of existing GNN methods, however, assume brain graphs are static over time and the graph adjacency matrix is known prior to model training. These assumptions are at odds with neuroscientific evidence that brain graphs are time-varying with a connectivity structure that depends on the choice of functional connectivity measure. Noisy brain graphs that do not truly represent the underling fMRI data can have a detrimental impact on the performance of GNNs. As a solution, we propose Dynamic Brain Graph Structure Learning (DBGSL), a novel method for learning the optimal time-varying dependency structure of fMRI data induced by a downstream prediction task. Experiments demonstrate DBGSL achieves state-of-the-art performance for sex classification using real-world resting-state and task fMRI data. Moreover, analysis of the learnt dynamic graphs highlights prediction-related brain regions which align with existing neuroscience literature.
翻译:最近,图形神经网络(GNNS)在学习从功能磁共振成像(fMRI)数据中得出的脑图的显示方面表现出成功。但是,现有的大多数GNN方法假设脑图是长期静止的,而图对称矩阵在模型培训之前是已知的。这些假设与神经科学证据不符,神经科学证据表明脑图与连接结构的时间变化取决于功能连接测量的选择。不真正代表功能磁共振成像(FMRI)数据下方的神经脑图可能对GNS的性能产生有害影响。作为一种解决办法,我们提出动态脑图结构学习(DBGSL),这是学习由下游预测任务引发的FMRI数据的最佳时间变化依赖结构的一种新颖方法。实验表明DBGSL在使用真实世界的休息状态和任务FMRI数据进行性分类方面达到了最新水平的性表现。此外,对所学过的动态图的分析突出了与现有神经科学文献相一致的预测相关的脑区域。