Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; MD/PhD Program, Yale School of Medicine, New Haven, CT, USA.
Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA.
Cell Rep. 2020 Aug 25;32(8):108066. doi: 10.1016/j.celrep.2020.108066.
Functional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but how tasks have this effect is unknown. In over 700 individuals performing seven tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that task-induced changes in FC successfully predict phenotype, and these changes are not simply driven by task activation. Activation, however, is useful for prediction only if the in-scanner task is related to the predicted phenotype. To further characterize these predictive FC changes, we develop and apply an inter-subject PPI analysis. We find that moderate, but not high, task-induced consistency of the blood-oxygen-level-dependent (BOLD) signal across individuals is useful for prediction. Together, these findings demonstrate that in-scanner tasks have distributed, phenotypically relevant effects on brain functional organization, and they offer a framework to leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.
功能连接(FC)是从任务 fMRI 数据中计算出来的,比基于静息状态的 FC 更能揭示大脑-表型关系,但任务如何产生这种影响尚不清楚。在超过 700 名个体执行七个任务的过程中,我们使用心理生理交互(PPI)和预测建模分析来证明,FC 的任务诱导变化可以成功预测表型,并且这些变化不是简单地由任务激活驱动的。然而,只有当扫描过程中的任务与预测的表型相关时,激活才对预测有用。为了进一步描述这些具有预测性的 FC 变化,我们开发并应用了一种跨主体 PPI 分析。我们发现,个体之间中等但不是高度的任务诱导的血氧水平依赖(BOLD)信号一致性对于预测是有用的。总的来说,这些发现表明,扫描过程中的任务对大脑功能组织有分布式的、与表型相关的影响,它们提供了一个框架,可以利用任务激活和 FC 来揭示复杂的人类特征、症状和行为的神经基础。