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本文引用的文献

1
Mapping how local perturbations influence systems-level brain dynamics.绘制局部扰动如何影响系统级大脑动力学的图谱。
Neuroimage. 2017 Oct 15;160:97-112. doi: 10.1016/j.neuroimage.2017.01.057. Epub 2017 Jan 24.
2
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance.功能性脑网络的动力学:认知任务执行过程中的整合网络状态
Neuron. 2016 Oct 19;92(2):544-554. doi: 10.1016/j.neuron.2016.09.018. Epub 2016 Sep 29.
3
The Rediscovery of Slowness: Exploring the Timing of Cognition.《慢的再发现:探索认知的时间性》
Trends Cogn Sci. 2015 Oct;19(10):616-628. doi: 10.1016/j.tics.2015.07.011.
4
A Dynamic Core Network and Global Efficiency in the Resting Human Brain.静息态人脑的动态核心网络与全局效率
Cereb Cortex. 2016 Oct;26(10):4015-33. doi: 10.1093/cercor/bhv185. Epub 2015 Sep 6.
5
Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks.瞬态脑活动从空间和时间上重叠的网络角度解析了功能磁共振成像静息态动力学。
Nat Commun. 2015 Jul 16;6:7751. doi: 10.1038/ncomms8751.
6
Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns.利用简短的全脑功能连接模式追踪个体的持续认知。
Proc Natl Acad Sci U S A. 2015 Jul 14;112(28):8762-7. doi: 10.1073/pnas.1501242112. Epub 2015 Jun 29.
7
Ongoing dynamics in large-scale functional connectivity predict perception.大规模功能连接性中的持续动态变化可预测感知。
Proc Natl Acad Sci U S A. 2015 Jul 7;112(27):8463-8. doi: 10.1073/pnas.1420687112. Epub 2015 Jun 23.
8
Learning-induced autonomy of sensorimotor systems.学习诱导的感觉运动系统自主性
Nat Neurosci. 2015 May;18(5):744-51. doi: 10.1038/nn.3993. Epub 2015 Apr 6.
9
Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations.静居于富集俱乐部:慢皮层波动的脑网络决定因素
Philos Trans R Soc Lond B Biol Sci. 2015 May 19;370(1668). doi: 10.1098/rstb.2014.0165.
10
Towards a statistical test for functional connectivity dynamics.针对功能连接动态的统计检验方法。
Neuroimage. 2015 Jul 1;114:466-70. doi: 10.1016/j.neuroimage.2015.03.047. Epub 2015 Mar 25.

视觉刺激的神经解码随全局网络效率的波动而变化。

Neural decoding of visual stimuli varies with fluctuations in global network efficiency.

作者信息

Cocchi Luca, Yang Zhengyi, Zalesky Andrew, Stelzer Johannes, Hearne Luke J, Gollo Leonardo L, Mattingley Jason B

机构信息

Queensland Brain Institute, The University of Queensland, Brisbane, Australia.

QIMR Berghofer Medical Research Institute, Brisbane, Australia.

出版信息

Hum Brain Mapp. 2017 Jun;38(6):3069-3080. doi: 10.1002/hbm.23574. Epub 2017 Mar 25.

DOI:10.1002/hbm.23574
PMID:28342260
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6866876/
Abstract

Functional magnetic resonance imaging (fMRI) studies have shown that neural activity fluctuates spontaneously between different states of global synchronization over a timescale of several seconds. Such fluctuations generate transient states of high and low correlation across distributed cortical areas. It has been hypothesized that such fluctuations in global efficiency might alter patterns of activity in local neuronal populations elicited by changes in incoming sensory stimuli. To test this prediction, we used a linear decoder to discriminate patterns of neural activity elicited by face and motion stimuli presented periodically while participants underwent time-resolved fMRI. As predicted, decoding was reliably higher during states of high global efficiency than during states of low efficiency, and this difference was evident across both visual and nonvisual cortical regions. The results indicate that slow fluctuations in global network efficiency are associated with variations in the pattern of activity across widespread cortical regions responsible for representing distinct categories of visual stimulus. More broadly, the findings highlight the importance of understanding the impact of global fluctuations in functional connectivity on specialized, stimulus driven neural processes. Hum Brain Mapp 38:3069-3080, 2017. © 2017 Wiley Periodicals, Inc.

摘要

功能磁共振成像(fMRI)研究表明,神经活动在几秒的时间尺度上会在不同的全局同步状态之间自发波动。这种波动会在分布的皮层区域产生高相关性和低相关性的瞬态状态。据推测,这种全局效率的波动可能会改变由传入感觉刺激变化引发的局部神经元群体的活动模式。为了验证这一预测,我们使用线性解码器来区分参与者在进行时间分辨fMRI时周期性呈现的面部和运动刺激所引发的神经活动模式。正如预测的那样,在全局效率高的状态下解码的可靠性要高于低效率状态,并且这种差异在视觉和非视觉皮层区域都很明显。结果表明,全局网络效率的缓慢波动与负责表征不同类别视觉刺激的广泛皮层区域的活动模式变化有关。更广泛地说,这些发现凸显了理解功能连接中的全局波动对专门的、刺激驱动的神经过程的影响的重要性。《人类大脑图谱》38:3069 - 3080,2017年。© 2017威利期刊公司。