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

机译:动态因果控制信号在高阶认知中的发育成熟:神经认知网络模型

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摘要

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.
机译:认知技能会经历长期的发展变化,从而导致熟练程度的提高,这是人类认知的特征。随着时间的推移发展的一项技能是解决问题的能力,而解决问题的能力又依赖于认知控制和注意力的能力。在这里,我们使用一种新颖的基于多模态神经认知网络的方法,将与任务相关的功能磁共振成像,静止状态功能磁共振成像和扩散张量成像(DTI)相结合,以研究7-9岁儿童解决问题的基本控制过程。我们的分析着重于两个关键的神经认知网络,这些神经网络牵涉到广泛的认知任务中,包括控制:岛突扣带显着网络,锚固在前岛(AI),腹侧前额叶皮层和前扣带回皮质,以及额顶中央执行网络,固定在背外侧前额叶皮层和后顶叶皮层(PPC)中。我们发现,到9岁时,显着网络的AI节点是解决问题期间引发控制信号的主要因果枢纽。至关重要的是,尽管AI激活更强,但从AI到中央执行网络的PPC节点的因果调节影响的强度明显较弱,并导致儿童的行为表现水平低于成人。使用两种不同的分析方法对fMRI数据中的因果相互作用进行估计,验证了这些结果。同时,基于DTI的医学影像学显示儿童AI-PPC结构连接性较弱。我们的研究结果指出,人工智能连接及其因果关系的跨网络影响在动态的自上而下的控制信号逐渐成熟的基础上起着至关重要的作用。总体而言,我们的研究表明,与多模式成像相结合时,统一的神经认知网络模型如何增强我们对单个任务激活灶以外的泛化能力,并为阐明大脑和认知发展的关键特征提供一个通用框架。所开发的定量方法可能在研究神经控制障碍(例如自闭症和多动症)受损的神经发育障碍中很有用。

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