Qu Shijie, Qu Yueyue Lydia, Yoo Kwangsun, Chun Marvin M
Department of Psychology, Yale University, New Haven, CT, USA.
Wu Tsai Institute, Yale University, New Haven, CT, USA.
bioRxiv. 2025 Feb 9:2024.10.21.619468. doi: 10.1101/2024.10.21.619468.
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 research has examined the nature of different components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. 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 performance, 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的个体差异。在这里，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，构建了脑网络模型，以成功预测在侧翼任务、维度变化卡片分类任务和2-back任务上的个体表现差异，每个任务分别假定对应于抑制、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）数据在预测性能上优于静息态和容积数据后，我们重点关注这些数据。高跨任务预测准确性以及典型网络（如额顶叶和默认模式网络）的联合募集，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，可以从紧密位于额顶叶、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性能，表明其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认知结构的局限性，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。
