Executive functions (EFs) refer to a range of cognitive control and regulation processes that coordinate thoughts and actions in a goal-directed way and are regarded as a hallmark of intellectual abilities. However, most studies have used a single measurement to explore the relationship between EFs and intelligence, and there is a lack of robust evidence to demonstrate the link between EF-related neural substrates and intelligence under an integrative framework. To address this issue, we employed a large sample (primary dataset, n = 881; hold-out dataset, n = 181) from the Human Connectome Project, which included high-quality MRI data and multiple EF and intelligence measurements. Based on a machine learning framework, we examined the predictive effect of EF-related functional connectivity (FC) on fluid intelligence (Gf) and crystallized intelligence (Gc) using a connectome-based predictive model. The results showed that all types of EF-related FCs (i.e., EF-common, updating-, shifting-, and inhibition-specific FCs) predicted novel subjects' Gf and Gc in the primary dataset and successfully generalized to the hold-out dataset. Additionally, EF-related FCs appeared to demonstrate better performance in predicting Gc. Identified predictive FCs revealed the domain-general and domain-specific connectivity patterns of EFs, and the network hubs were mainly located in the default mode, cognitive control, salience, and visual networks. These findings facilitate our understanding of the relation between multiple EF domains and intelligence from the perspective of network neuroscience, suggesting that different intellectual abilities and EFs share similar neural bases to some extent, which allows the link between EFs and intelligence to be revisited.

执行功能（EF）是指一系列认知控制和调节过程，这些过程以目标为导向的方式协调思想和行动，并被视为智力能力的标志。但是，大多数研究都使用单一测量方法来探索EF与智力之间的关系，并且缺乏有力的证据来证明在整合框架下EF相关神经底物与智力之间的联系。为了解决这个问题，我们采用了一个大样本（主要数据集，n  = 881；支持数据集，n = 181），来自“人类Connectome项目”，其中包括高质量MRI数据以及多个EF和智能测量。基于机器学习框架，我们使用基于连接器的预测模型检查了EF相关功能连接（FC）对流体智能（Gf）和结晶智能（Gc）的预测效果。结果表明，所有类型的与EF相关的FC（即EF常见，更新，转移和抑制特定的FC）都可以在主要数据集中预测新受试者的Gf和Gc，并成功地推广到支持数据集。此外，与EF相关的FC在预测Gc方面似乎表现出更好的性能。识别出的预测性FC揭示了EF的通用域和特定域的连接模式，并且网络集线器主要位于默认模式，认知控制，显着性和视觉网络。这些发现有助于我们从网络神经科学的角度理解多个EF域与智能之间的关系，这表明不同的智力和EF在一定程度上共享相似的神经基础，这使得我们可以重新研究EF与智能之间的联系。