论文标题
可解释的AI，用于将大脑结构和功能连接归因于
Interpretable AI for relating brain structural and functional connectomes
论文作者
论文摘要
神经科学的核心问题之一是了解大脑结构与功能的关系。天真的人可以将感兴趣的大脑区域（ROI）之间的白质纤维道的直接连接与同一对ROI的共同激活增加，但是事实证明，结构和功能连接组（SCS和FCS）之间的联系已被证明更为复杂。为了学习一种表征SCS，FCS和SC-FC耦合中种群变化的现实生成模型，我们开发了一个我们称为Staf-Gate的图形自动编码器。我们培训了来自人类连接项目（HCP）的数据，并在预测SC和FC的联合生成方面显示了最先进的性能。另外，作为所提出方法的关键组成部分，我们提供了一种基于掩蔽的算法来提取有关SC-FC耦合的可解释推断。我们的解释方法确定了用于FC耦合以及SC和FC与性别有关的重要SC子网。
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.
