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功能磁共振成像识别的活跃和不活跃区域都会重新配置以支持任务执行。

Both activated and less-activated regions identified by functional MRI reconfigure to support task executions.

机构信息

Brainnetome CenterInstitute of Automation Chinese Academy of Sciences Beijing China.

National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China.

出版信息

Brain Behav. 2017 Dec 20;8(1):e00893. doi: 10.1002/brb3.893. eCollection 2018 Jan.

Abstract

INTRODUCTION

Functional magnetic resonance imaging (fMRI) has become very important for noninvasively characterizing BOLD signal fluctuations, which reflect the changes in neuronal firings in the brain. Unlike the activation detection strategy utilized with fMRI, which only emphasizes the synchronicity between the functional nodes (activated regions) and the task design, brain connectivity and network theory are able to decipher the interactive structure across the entire brain. However, little is known about whether and how the activated/less-activated interactions are associated with the functional changes that occur when the brain changes from the resting state to a task state. What are the key networks that play important roles in the brain state changes?

METHODS

We used the fMRI data from the Human Connectome Project S500 release to examine the changes of network efficiency, interaction strength, and fractional modularity contributions of both the local and global networks, when the subjects change from the resting state to seven different task states.

RESULTS

We found that, from the resting state to each of seven task states, both the activated and less-activated regions had significantly changed to be in line with, and comparably contributed to, a global network reconfiguration. We also found that three networks, the default mode network, frontoparietal network, and salience network, dominated the flexible reconfiguration of the brain.

CONCLUSIONS

This study shows quantitatively that contributions from both activated and less-activated regions enable the global functional network to respond when the brain switches from the resting state to a task state and suggests the necessity of considering large-scale networks (rather than only activated regions) when investigating brain functions in imaging cognitive neuroscience.

摘要

简介

功能磁共振成像(fMRI)在无创性地描述反映大脑神经元活动变化的血氧水平依赖信号波动方面变得非常重要。与 fMRI 所采用的仅强调功能节点(激活区域)与任务设计之间的同步性的激活检测策略不同,脑连接和网络理论能够破译整个大脑之间的交互结构。然而,人们对于激活/不活跃的相互作用是否以及如何与大脑从静息状态转变为任务状态时发生的功能变化相关知之甚少。在大脑状态变化中起重要作用的关键网络有哪些?

方法

我们使用人类连接组计划 S500 版本的 fMRI 数据,来检测当被试从静息状态转变为七种不同的任务状态时,局部和全局网络的网络效率、相互作用强度和分数模块性的变化。

结果

我们发现,从静息状态到七种任务状态中的每一种状态,激活和不活跃区域都发生了显著的变化,以与全局网络的重新配置保持一致,并做出了相当的贡献。我们还发现,三个网络,即默认模式网络、额顶网络和突显网络,主导了大脑的灵活重新配置。

结论

这项研究定量地表明,激活和不活跃区域的贡献使得全局功能网络能够在大脑从静息状态切换到任务状态时做出反应,并表明在影像认知神经科学中研究大脑功能时需要考虑大规模网络(而不仅仅是激活区域)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a24/5853621/df54eba2df56/BRB3-8-e00893-g001.jpg

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