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默认网络中的二分功能划分支持不同形式的内部导向认知。

Bipartite Functional Fractionation within the Default Network Supports Disparate Forms of Internally Oriented Cognition.

机构信息

MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge UK.

出版信息

Cereb Cortex. 2020 Sep 3;30(10):5484-5501. doi: 10.1093/cercor/bhaa130.

Abstract

Our understanding about the functionality of the brain's default network (DN) has significantly evolved over the past decade. Whereas traditional views define this network based on its suspension/disengagement during task-oriented behavior, contemporary accounts have characterized various situations wherein the DN actively contributes to task performance. However, it is unclear how different task-contexts drive componential regions of the DN to coalesce into a unitary network and fractionate into different subnetworks. Here we report a compendium of evidence that provides answers to these questions. Across multiple analyses, we found a striking dyadic structure within the DN in terms of the profiles of task-triggered fMRI response and effective connectivity, significantly extending beyond previous inferences based on meta-analysis and resting-state activities. In this dichotomy, one subset of DN regions prefers mental activities "interfacing with" perceptible events, while the other subset prefers activities "detached from" perceptible events. While both show a common "aversion" to sensory-motoric activities, their differential preferences manifest a subdivision that sheds light upon the taxonomy of the brain's memory systems. This dichotomy is consistent with proposals of a macroscale gradational structure spanning across the cerebrum. This gradient increases its representational complexity, from primitive sensory-motoric processing, through lexical-semantic representations, to elaborated self-generated thoughts.

摘要

在过去的十年中,我们对大脑默认网络(DN)功能的理解有了显著的发展。传统观点基于其在任务导向行为中的暂停/脱离来定义这个网络,而当代的观点则描述了各种情况下 DN 如何主动促进任务表现。然而,不同的任务情境如何驱动 DN 的组成区域凝聚成一个单一的网络并分裂成不同的子网仍然不清楚。在这里,我们报告了一份证据汇编,为这些问题提供了答案。在多项分析中,我们发现 DN 内部存在一种引人注目的二元结构,表现在任务触发 fMRI 反应和有效连接的特征上,这大大超出了基于元分析和静息状态活动的先前推断。在这种二分法中,DN 的一部分区域更喜欢与可感知事件“接口”的心理活动,而另一部分区域则更喜欢与可感知事件“分离”的活动。虽然两者都表现出对感觉运动活动的共同“厌恶”,但它们的不同偏好表现出一种细分,这为大脑记忆系统的分类学提供了启示。这种二分法与横跨大脑的宏观梯度结构的提议一致。这个梯度增加了其表示的复杂性,从原始的感觉运动处理,通过词汇语义表示,到精心制作的自我产生的思想。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9929/7472201/b28e93d01f23/bhaa130f1.jpg

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