One of the central problems in neuroscience is understanding how brain structure relates to function. Naively one can relate the direct connections of white matter fiber tracts between brain regions of interest (ROIs) to the increased co-activation in the same pair of ROIs, but the link between structural and functional connectomes (SCs and FCs) has proven to be much more complex. To learn a realistic generative model characterizing population variation in SCs, FCs, and the SC-FC coupling, we develop a graph auto-encoder that we refer to as Staf-GATE. We trained Staf-GATE with data from the Human Connectome Project (HCP) and show state-of-the-art performance in predicting FC and joint generation of SC and FC. In addition, as a crucial component of the proposed approach, we provide a masking-based algorithm to extract interpretable inferences about SC-FC coupling. Our interpretation methods identified important SC subnetworks for FC coupling and relating SC and FC with sex.

翻译：神经科学的核心问题之一是理解大脑结构如何与功能相关联。直观上，可以将感兴趣脑区（ROIs）之间白质纤维束的直接连接与同一对ROI内的共激活增强联系起来，但结构和功能连接组（SC和FC）之间的关联已被证明要复杂得多。为学习一个能刻画SC、FC及SC-FC耦合中群体变异的现实生成模型，我们开发了一种图自编码器，称之为Staf-GATE。我们利用人类连接组计划（HCP）的数据训练Staf-GATE，并在预测FC以及联合生成SC和FC方面展现了最先进的性能。此外，作为所提方法的关键组成部分，我们提供了一种基于掩码的算法，用于提取关于SC-FC耦合的可解释性推断。我们的解释方法识别出对FC耦合至关重要的SC子网络，并将SC和FC与性别特征相关联。