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海马体和内侧前额叶皮层对全脑静息状态连接的调节与流体智力有关。

Brain-wide resting-state connectivity regulation by the hippocampus and medial prefrontal cortex is associated with fluid intelligence.

机构信息

CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.

Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.

出版信息

Brain Struct Funct. 2020 Jun;225(5):1587-1600. doi: 10.1007/s00429-020-02077-8. Epub 2020 Apr 24.

Abstract

The connectivity hub property of the hippocampus (HIP) and the medial prefrontal cortex (MPFC) is essential for their widespread involvement in cognition; however, the cooperation mechanism between them is far from clear. Herein, using resting-state functional MRI and Gaussian Bayesian network to describe the directed organizing architecture of the HIP-MPFC pathway with regions in the brain, we demonstrated that the HIP and the MPFC have central roles as the driving hub and aggregating hub, respectively. The status of the HIP and the MPFC is dominant in communications between the HIP and the default-mode network, between the HIP and core neurocognitive networks, including the default-mode, frontoparietal, and salience networks, and between brain-wide representative regions, suggesting a strong and robust central position of the two regions in regulating the dynamics of large-scale brain activity. Furthermore, we found that the directed connectivity and flow from the right HIP to the MPFC is significantly linked to fluid intelligence. Together, these results clarify the different roles of the HIP and the MPFC that jointly contribute to network dynamics and cognitive ability from a data-driven insight via the use of the directed connectivity method.

摘要

海马体(HIP)和内侧前额叶皮层(MPFC)的连接枢纽特性对于它们广泛参与认知至关重要;然而,它们之间的合作机制尚不清楚。在此,我们使用静息态功能磁共振成像和高斯贝叶斯网络来描述脑区中海马体-MPFC 通路的有向组织架构,结果表明 HIP 和 MPFC 分别作为驱动枢纽和聚合枢纽发挥核心作用。HIP 和 MPFC 的状态在 HIP 与默认模式网络之间、HIP 与核心神经认知网络(包括默认模式、额顶叶和突显网络)之间以及全脑代表性区域之间的通信中占据主导地位,这表明这两个区域在调节大脑活动的动态方面具有强大而稳健的中心地位。此外,我们发现来自右侧 HIP 到 MPFC 的有向连接和流显著与流体智力相关。总之,这些结果通过使用有向连接方法从数据驱动的角度阐明了 HIP 和 MPFC 的不同作用,它们共同为网络动态和认知能力做出贡献。

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