Developmental Maturation of Dynamic Causal Control Signals in Higher-Order Cognition: A Neurocognitive Network Model

Cognitive skills undergo protracted developmental changes resulting in proficiencies that are a hallmark of human cognition. One skill that develops over time is the ability to problem solve, which in turn relies on cognitive control and attention abilities. Here we use a novel multimodal neurocognitive network-based approach combining task-related fMRI, resting-state fMRI and diffusion tensor imaging (DTI) to investigate the maturation of control processes underlying problem solving skills in 7–9 year-old children. Our analysis focused on two key neurocognitive networks implicated in a wide range of cognitive tasks including control: the insula-cingulate salience network, anchored in anterior insula (AI), ventrolateral prefrontal cortex and anterior cingulate cortex, and the fronto-parietal central executive network, anchored in dorsolateral prefrontal cortex and posterior parietal cortex (PPC). We found that, by age 9, the AI node of the salience network is a major causal hub initiating control signals during problem solving. Critically, despite stronger AI activation, the strength of causal regulatory influences from AI to the PPC node of the central executive network was significantly weaker and contributed to lower levels of behavioral performance in children compared to adults. These results were validated using two different analytic methods for estimating causal interactions in fMRI data. In parallel, DTI-based tractography revealed weaker AI-PPC structural connectivity in children. Our findings point to a crucial role of AI connectivity, and its causal cross-network influences, in the maturation of dynamic top-down control signals underlying cognitive development. Overall, our study demonstrates how a unified neurocognitive network model when combined with multimodal imaging enhances our ability to generalize beyond individual task-activated foci and provides a common framework for elucidating key features of brain and cognitive development. The quantitative approach developed is likely to be useful in investigating neurodevelopmental disorders, in which control processes are impaired, such as autism and ADHD.

认知技能会经历漫长的发育变化，最终形成的熟练能力是人类认知的标志性特征。随时间发展的一项核心认知技能便是问题解决能力，而该能力又依赖于认知控制与注意功能。本研究采用一种全新的多模态神经认知网络分析方法，结合任务态功能磁共振成像（task-related fMRI）、静息态功能磁共振成像（resting-state fMRI）与弥散张量成像（DTI），探究7至9岁儿童问题解决能力背后的控制加工发育成熟机制。本次分析聚焦于两类广泛参与包括控制加工在内的多种认知任务的关键神经认知网络：一是以前脑岛（AI）、腹外侧前额叶皮层与前扣带回皮层为锚点的岛叶扣带回突显网络；二是以背外侧前额叶皮层与后顶叶皮层（PPC）为锚点的额顶中央执行网络。研究发现，至9岁时，突显网络的AI节点已成为问题解决过程中发起控制信号的核心因果枢纽。尤为关键的是，尽管儿童的AI激活强度更高，但从AI至中央执行网络PPC节点的因果调控作用强度却显著更弱，且相较于成人，这一差异导致儿童的行为表现水平更低。本研究采用两种不同的fMRI数据因果交互估计分析方法对上述结果进行了验证。与此同时，基于DTI的纤维束追踪结果显示，儿童的AI-PPC结构连接强度更弱。本研究结果表明，AI连接及其跨网络因果调控作用，在认知发育背后的动态自上而下控制信号成熟过程中发挥着至关重要的作用。总体而言，本研究证实，将统一的神经认知网络模型与多模态成像技术相结合，能够提升我们超越单个任务激活焦点进行泛化推断的能力，并为阐明大脑与认知发育的关键特征提供了通用框架。本研究开发的量化分析方法，或可用于探究存在控制加工受损的神经发育障碍，如孤独症与注意缺陷多动障碍（ADHD）。