Center for Mind & Brain Sciences and Institute of Interdisciplinary Studies, Hunan Normal University, Hunan, Changsha, 410081, China; Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; China National Clinical Research Center for Mental Disorders (Xiangya), Changsha, Hunan, China.
Center for Mind & Brain Sciences and Institute of Interdisciplinary Studies, Hunan Normal University, Hunan, Changsha, 410081, China.
Brain Cogn. 2024 Nov;181:106221. doi: 10.1016/j.bandc.2024.106221. Epub 2024 Sep 8.
Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks.
The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control.
Our results showed that both structural (r values 0.263-0.375) and functional (r values 0.336-0.503) connectomes can significantly predict individuals' cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns.
The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.
认知控制涉及灵活配置心理资源和调整行为以实现目标导向的动作。它与大脑网络的协调活动有关，尽管目前尚不清楚结构和功能大脑网络如何能够预测认知控制。基于连接组学的预测建模 (CPM) 是一种基于大脑网络预测认知控制的强大工具。
本研究使用 CPM 从 UCLA 神经精神表型联盟数据集的 102 名健康成年人中预测认知控制，并进一步比较支持认知控制的结构和功能连接组特征。
我们的结果表明，结构（r 值 0.263-0.375）和功能（r 值 0.336-0.503）连接组都可以显著预测个体的认知控制子成分。所有三个认知控制子成分的功能和结构网络之间存在重叠，特别是在额顶（FP）和运动（Mot）网络中，而每个子成分也有其自身独特的权重预测网络。总体而言，支持不同认知控制子成分的功能和结构连通性表现出重叠和独特的空间模式。
结构和功能连接组为预测认知控制能力提供了互补信息。整合来自两个连接组的信息可以更全面地了解认知控制的神经基础。
