Department of Psychology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
Department of Psychology, University of Texas at Austin, Austin, TX 78712.
Proc Natl Acad Sci U S A. 2018 Feb 13;115(7):E1598-E1607. doi: 10.1073/pnas.1715766115. Epub 2018 Jan 30.
The frontoparietal control network (FPCN) plays a central role in executive control. It has been predominantly viewed as a unitary domain general system. Here, we examined patterns of FPCN functional connectivity (FC) across multiple conditions of varying cognitive demands, to test for FPCN heterogeneity. We identified two distinct subsystems within the FPCN based on hierarchical clustering and machine learning classification analyses of within-FPCN FC patterns. These two FPCN subsystems exhibited distinct patterns of FC with the default network (DN) and the dorsal attention network (DAN). FPCN exhibited stronger connectivity with the DN than the DAN, whereas FPCN exhibited the opposite pattern. This twofold FPCN differentiation was observed across four independent datasets, across nine different conditions (rest and eight tasks), at the level of individual-participant data, as well as in meta-analytic coactivation patterns. Notably, the extent of FPCN differentiation varied across conditions, suggesting flexible adaptation to task demands. Finally, we used meta-analytic tools to identify several functional domains associated with the DN and DAN that differentially predict activation in the FPCN subsystems. These findings reveal a flexible and heterogeneous FPCN organization that may in part emerge from separable DN and DAN processing streams. We propose that FPCN may be preferentially involved in the regulation of introspective processes, whereas FPCN may be preferentially involved in the regulation of visuospatial perceptual attention.
额顶控制网络(FPCN)在执行控制中起着核心作用。它主要被视为一个单一的、领域一般性的系统。在这里,我们研究了在不同认知需求的多种条件下 FPCN 功能连接(FC)的模式,以检验 FPCN 的异质性。我们基于 FPCN 内 FC 模式的层次聚类和机器学习分类分析,在 FPCN 内确定了两个不同的子系统。这两个 FPCN 子系统与默认网络(DN)和背侧注意网络(DAN)表现出不同的 FC 模式。FPCN 与 DN 的连接比 DAN 更强,而 FPCN 则表现出相反的模式。这种双重的 FPCN 分化在四个独立的数据集、九个不同的条件(休息和八个任务)中都得到了观察,其水平为个体参与者数据,以及在元分析的共激活模式中。值得注意的是,FPCN 的分化程度因条件而异,这表明其可以灵活适应任务需求。最后,我们使用元分析工具来识别与 DN 和 DAN 相关的几个功能域,这些功能域可以预测 FPCN 子系统的激活情况。这些发现揭示了一个灵活的、异质的 FPCN 组织,它可能部分源于可分离的 DN 和 DAN 处理流。我们提出,FPCN 可能更倾向于参与内省过程的调节,而 FPCN 可能更倾向于参与视觉空间感知注意的调节。