Task-based fMRI uses actions or stimuli to trigger task-specific brain
responses and measures them using BOLD contrast. Despite the significant
task-induced spatiotemporal brain activation fluctuations, most studies on
task-based fMRI ignore the task context information aligned with fMRI and
consider task-based fMRI a coherent sequence. In this paper, we show that using
the task structures as data-driven guidance is effective for spatiotemporal
analysis. We propose STNAGNN, a GNN-based spatiotemporal architecture, and
validate its performance in an autism classification task. The trained model is
also interpreted for identifying autism-related spatiotemporal brain
biomarkers.

使用基于任务的功能磁共振成像 (task-based fMRI) 方法，结合 BOLD 对比度，基于任务的脑部反应进行衡量，本研究利用数据驱动的任务结构为指导，在自动识别孤独症任务中展示出空间时间分析的有效性，并提出了 STNAGNN 这个基于图神经网络的空间时间架构，用于解释和识别孤独症相关的空间时间性脑生物标记物。