In this paper, we introduce a new Bayesian approach for analyzing task fMRI data that simultaneously detects activation signatures and background connectivity. Our modeling involves a new hybrid tensor spatial-temporal basis strategy that enables scalable computing yet captures nearby and distant intervoxel correlation and long-memory temporal correlation. The spatial basis involves a composite hybrid transform with two levels: the first accounts for within-ROI correlation, and second between-ROI distant correlation. We demonstrate in simulations how our basis space regression modeling strategy increases sensitivity for identifying activation signatures, partly driven by the induced background connectivity that itself can be summarized to reveal biological insights. This strategy leads to computationally scalable fully Bayesian inference at the voxel or ROI level that adjusts for multiple testing. We apply this model to Human Connectome Project data to reveal insights into brain activation patterns and background connectivity related to working memory tasks.

翻译：在本文中,我们引入了一种新的巴伊西亚分析任务FMRI数据的方法,该方法同时检测激活信号和背景连通性。我们的建模涉及一种新的混合高时空空间基基战略,它能够进行可缩放计算,但能够捕捉附近和远处的互交关系和长线时间相关关系。空间基础包含一种复合混合转换,分两个层次:ROI内部关联的第一个账户,以及ROI远程关联的第二个。我们在模拟中展示了我们的基础空间回归模型战略如何提高识别激活信号的敏感度,这部分是由可被总结以揭示生物洞察力的诱导背景连通性所驱动的。这一战略导致在对多个测试进行调整的 voxel 或 ROI 水平上进行可计算可缩放的全巴伊瑟推断。我们将这一模型应用于人类连接项目数据,以揭示与工作记忆任务相关的大脑激活模式和背景连通性的洞察力。