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大规模静息态网络中的信息流方向取决于频率。

Direction of information flow in large-scale resting-state networks is frequency-dependent.

作者信息

Hillebrand Arjan, Tewarie Prejaas, van Dellen Edwin, Yu Meichen, Carbo Ellen W S, Douw Linda, Gouw Alida A, van Straaten Elisabeth C W, Stam Cornelis J

机构信息

Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, 1081 HV Amsterdam, The Netherlands;

Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, 1081 HV Amsterdam, The Netherlands; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands;

出版信息

Proc Natl Acad Sci U S A. 2016 Apr 5;113(14):3867-72. doi: 10.1073/pnas.1515657113. Epub 2016 Mar 21.

Abstract

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

摘要

正常的脑功能需要空间上分离且功能专门化的宏观区域之间进行相互作用,然而这些相互作用在大规模功能网络中的方向性尚不清楚。我们使用脑磁图来确定这些相互作用的方向性,其中方向性是通过波束形成器重建的神经元激活估计值的时间序列,并使用最近提出的相位转移熵测量方法来推断的。我们观察到在高频带(α1、α2和β波段)中存在组织良好的从后到前的信息流模式,主要由视觉皮层和后默认模式网络中的区域主导。在θ波段发现了相反的从前到后的流动模式,主要涉及额叶中向更广泛分布的网络发送信息的区域。θ波段中的许多强信息发送者也是α2波段中的频繁接收者,反之亦然。我们的结果提供了证据,表明人类大脑中大规模静息态信息流模式形成了频率依赖性的折返回路,在高频带中由顶枕叶皮层向整合性额叶区域的流动主导,而θ波段从前到后的流动则与之镜像对应。

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本文引用的文献

1
General relationship of global topology, local dynamics, and directionality in large-scale brain networks.
PLoS Comput Biol. 2015 Apr 14;11(4):e1004225. doi: 10.1371/journal.pcbi.1004225. eCollection 2015 Apr.
2
Source space estimation of oscillatory power and brain connectivity in tinnitus.
PLoS One. 2015 Mar 23;10(3):e0120123. doi: 10.1371/journal.pone.0120123. eCollection 2015.
3
The roles of cortical oscillations in sustained attention.
Trends Cogn Sci. 2015 Apr;19(4):188-95. doi: 10.1016/j.tics.2015.02.004. Epub 2015 Mar 9.
4
Not so different after all: The same oscillatory processes support different types of attention.
Brain Res. 2015 Nov 11;1626:183-97. doi: 10.1016/j.brainres.2015.02.017. Epub 2015 Feb 24.
5
Beta and gamma rhythms go with the flow.
Neuron. 2015 Jan 21;85(2):236-7. doi: 10.1016/j.neuron.2014.12.067.
6
Visual areas exert feedforward and feedback influences through distinct frequency channels.
Neuron. 2015 Jan 21;85(2):390-401. doi: 10.1016/j.neuron.2014.12.018. Epub 2014 Dec 31.
7
How to detect the Granger-causal flow direction in the presence of additive noise?
Neuroimage. 2015 Mar;108:301-18. doi: 10.1016/j.neuroimage.2014.12.017. Epub 2014 Dec 13.
8
Opportunities and methodological challenges in EEG and MEG resting state functional brain network research.
Clin Neurophysiol. 2015 Aug;126(8):1468-81. doi: 10.1016/j.clinph.2014.11.018. Epub 2014 Nov 28.
10
Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex.
Proc Natl Acad Sci U S A. 2014 Oct 7;111(40):14332-41. doi: 10.1073/pnas.1402773111. Epub 2014 Sep 9.

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