Qu Shijie, Qu Yueyue Lydia, Yoo Kwangsun, Chun Marvin M
Department of Psychology, Yale University, New Haven, Connecticut, USA.
Wu Tsai Institute, Yale University, New Haven, Connecticut, USA.
Hum Brain Mapp. 2025 Oct 1;46(14):e70358. doi: 10.1002/hbm.70358.
Executive functions, the set of cognitive control processes that facilitate adaptive thoughts and actions, are composed primarily of three distinct yet interrelated cognitive components: Inhibition, Shifting, and Updating. While prior brain research has examined the nature of different components as well as their interrelationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated task scores. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common executive function factor. To investigate the relationships among the three executive function components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared executive function component can be predicted from functional connectivity patterns densely located around the frontoparietal, default-mode, and dorsal attention networks. The Updating-specific component showed significant cross-prediction with the general executive function factor, suggesting a relatively stronger role than the other components. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction accuracy, indicating more distinct and specialized roles. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates a novel approach to infer common and specific components of executive function.
执行功能是一组促进适应性思维和行动的认知控制过程，主要由三个不同但相互关联的认知成分组成：抑制、转换和更新。虽然先前的脑研究已经考察了不同成分的性质及其相互关系，但较少有研究考察全脑连接性以预测这三个认知成分及相关任务分数的个体差异。在此，我们使用基于连接组的预测建模（CPM）方法和来自人类连接组计划的开放获取数据，构建脑网络模型，成功预测了在侧翼任务、维度变化卡片分类任务和2-back任务上的个体表现差异，这三个任务分别假定对应抑制、转换和更新。在确认2-back任务期间收集的灰质功能磁共振成像（fMRI）数据相对于静息态数据和体积数据具有更高的预测性能后，我们重点关注了这些数据。高跨任务预测准确性以及前额顶叶和默认模式网络等典型网络的联合激活，表明存在一个共同的执行功能因素。为了研究这三个执行功能成分之间的关系，我们开发了新的测量方法来区分它们的共同和独特方面。我们的分析证实，可以从密集分布在前额顶叶、默认模式和背侧注意网络周围的功能连接模式中预测出一个共同的执行功能成分。更新特定成分与一般执行功能因素显示出显著的交叉预测，表明其作用比其他成分相对更强。相比之下，转换特定成分和抑制特定成分表现出较低的交叉预测准确性，表明它们的作用更独特和专门化。鉴于个体行为测量不能纯粹反映预期的认知结构这一局限性，我们的研究展示了一种推断执行功能共同和特定成分的新方法。
