The current study sought to examine the functional connectivity of resting state networks (RSNs) as they relate to the individual domains of executive functioning (EF). Based on the Unity and Diversity model (Miyake et al., 2000), EF performance was captured using a three-factor model proposed by Karr et al. (2018), which includes inhibition, shifting, and fluency. Publicly available data was used from the Nathan Kline Institute -Rockland project was used. Of the 722 participants who completed the Delis-Kaplan Executive Function System (D-KEFS), which was used to measure EF performance, 269 of these individuals completed resting state fMRI scans. First, a confirmatory factory analysis replicated Karr et al. (2018) revealing three components: inhibition, shifting and fluency. Next, RSNs were identified across the sample using an Independent Components Analysis (ICA) and was compared to previously established intrinsic connectivity networks (Laird et al., 2011). Finally, dual regression was used to analyze the relationships between the functional connectivity of RSNs and EF performance, which indicated that RSNs were differentially associated with inhibition and shifting. Better inhibition was related to increased connectivity between the left striatum and the attentional control network. Better shifting performance was related to increased connectivity between the pre- and postcentral gyri and the speech and sensorimotor network. These results highlight individual differences within these RSNs that are unique to the literature, as non-EF confounds are mitigated within the current measurements of EF performance.

当前的研究试图检查静止状态网络（RSN）的功能连通性，因为它们与执行功能（EF）的各个领域相关。基于统一和多样性模型（Miyake等，2000），使用Karr等人提出的三因素模型来捕获EF性能。（2018），其中包括抑制，转移和流畅。使用Nathan Kline Institute -Rockland项目的公开数据。在完成用于测量EF性能的Delis-Kaplan执行功能系统（D-KEFS）的722名参与者中，其中269名参与者完成了静息状态fMRI扫描。首先，工厂验证分析重复了卡尔等人的观点。（2018）揭示了三个组成部分：抑制，转移和流畅。下一个，使用独立成分分析（ICA）在整个样本中识别出RSN，并将其与先前建立的内在连通性网络进行比较（Laird等，2011）。最后，使用双重回归分析RSN的功能连通性与EF性能之间的关系，这表明RSN与抑制和转移存在差异性关联。更好的抑制作用与左纹状体和注意控制网络之间的连通性增加有关。更好的换档性能与中央前后回旋以及语音和感觉运动网络之间的连通性增强有关。这些结果凸显了这些RSN中的文献差异，这是文献所独有的，因为在当前的EF性能测量中，非EF混杂已得到缓解。