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本文引用的文献

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Steady-state BOLD Response to Higher-order Cognition Modulates Low-Frequency Neural Oscillations.对高阶认知的稳态血氧水平依赖反应调节低频神经振荡。
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2
The frequency dimension of fMRI dynamic connectivity: Network connectivity, functional hubs and integration in the resting brain.功能磁共振成像动态连接性的频率维度:静息态大脑中的网络连接、功能枢纽与整合
Neuroimage. 2015 Nov 1;121:227-42. doi: 10.1016/j.neuroimage.2015.07.022. Epub 2015 Jul 11.
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Reliable Attention Network Scores and Mutually Inhibited Inter-network Relationships Revealed by Mixed Design and Non-orthogonal Method.混合设计和非正交方法揭示的可靠注意力网络分数及网络间相互抑制关系
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The brain's default mode network.大脑的默认模式网络。
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The brain's resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations.大脑的静息态活动由神经振荡的同步交叉频率耦合塑造。
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Disrupted cortical hubs in functional brain networks in social anxiety disorder.社交焦虑障碍中功能性脑网络的皮质枢纽中断。
Clin Neurophysiol. 2015 Sep;126(9):1711-6. doi: 10.1016/j.clinph.2014.11.014. Epub 2014 Nov 27.
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BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz.高于0.1 Hz时,静息态功能连接的血氧水平依赖分数贡献。
Neuroimage. 2015 Feb 15;107:207-218. doi: 10.1016/j.neuroimage.2014.12.012. Epub 2014 Dec 12.
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Steady-state BOLD response modulates low frequency neural oscillations.稳态血氧水平依赖反应调节低频神经振荡。
Sci Rep. 2014 Dec 9;4:7376. doi: 10.1038/srep07376.

低频稳态脑反应以频率特异性方式调节大规模功能网络。

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

作者信息

Wang Yi-Feng, Long Zhiliang, Cui Qian, Liu Feng, Jing Xiu-Juan, Chen Heng, Guo Xiao-Nan, Yan Jin H, Chen Hua-Fu

机构信息

Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.

School of Political Science and Public Administration, University of Electronic Science and Technology of China, Chengdu, 610054, China.

出版信息

Hum Brain Mapp. 2016 Jan;37(1):381-94. doi: 10.1002/hbm.23037. Epub 2015 Oct 29.

DOI:10.1002/hbm.23037
PMID:26512872
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6867441/
Abstract

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities.

摘要

神经振荡对大脑功能至关重要。研究表明,对于更综合和更远程的通信,神经振荡的频率较低。基于此,一些静息态研究表明,大规模网络在极低频范围(<1 Hz)发挥作用。然而,由于静息态研究和传统频率标记方法都无法在可控的认知活动中同时捕捉多个大规模网络,因此很难确定大脑网络的频率特征。在这项初步研究中,我们旨在检验大规模网络是否可以被任务诱发的低频稳态脑反应(lfSSBRs)以频率特异性模式进行调制。在一项修订后的注意力网络测试中,lfSSBRs在三重网络系统和感觉运动系统中被诱发,这表明大规模网络可以通过频率标记的方式进行调制。此外,网络间和网络内的同步以及相干性在基频和一次谐波处增加,而不是在其他频段,这表明信息通信存在频率特异性调制。然而,注意力条件之间没有差异,这表明lfSSBRs对一般注意力状态的调制比对区分注意力条件的调制要强得多。这项研究为lfSSBRs的优势和机制提供了见解。更重要的是,它为研究频率特异性大规模脑活动开辟了一条新途径。