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高于0.1 Hz时,静息态功能连接的血氧水平依赖分数贡献。

BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz.

作者信息

Chen Jingyuan E, Glover Gary H

机构信息

Department of Radiology, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.

Department of Radiology, Stanford University, Stanford, CA 94305, USA.

出版信息

Neuroimage. 2015 Feb 15;107:207-218. doi: 10.1016/j.neuroimage.2014.12.012. Epub 2014 Dec 12.

Abstract

Blood oxygen level dependent (BOLD) spontaneous signals from resting-state (RS) brains have typically been characterized by low-pass filtered timeseries at frequencies ≤ 0.1 Hz, and studies of these low-frequency fluctuations have contributed exceptional understanding of the baseline functions of our brain. Very recently, emerging evidence has demonstrated that spontaneous activities may persist in higher frequency bands (even up to 0.8 Hz), while presenting less variable network patterns across the scan duration. However, as an indirect measure of neuronal activity, BOLD signal results from an inherently slow hemodynamic process, which in fact might be too slow to accommodate the observed high-frequency functional connectivity (FC). To examine whether the observed high-frequency spontaneous FC originates from BOLD contrast, we collected RS data as a function of echo time (TE). Here we focus on two specific resting state networks - the default-mode network (DMN) and executive control network (ECN), and the major findings are fourfold: (1) we observed BOLD-like linear TE-dependence in the spontaneous activity at frequency bands up to 0.5 Hz (the maximum frequency that can be resolved with TR=1s), supporting neural relevance of the RSFC at a higher frequency range; (2) conventional models of hemodynamic response functions must be modified to support resting state BOLD contrast, especially at higher frequencies; (3) there are increased fractions of non-BOLD-like contributions to the RSFC above the conventional 0.1 Hz (non-BOLD/BOLD contrast at 0.4-0.5 Hz is ~4 times that at <0.1 Hz); and (4) the spatial patterns of RSFC are frequency-dependent. Possible mechanisms underlying the present findings and technical concerns regarding RSFC above 0.1 Hz are discussed.

摘要

静息态(RS)大脑的血氧水平依赖(BOLD)自发信号通常以频率≤0.1 Hz的低通滤波时间序列为特征,对这些低频波动的研究极大地增进了我们对大脑基线功能的理解。最近,新出现的证据表明,自发活动可能在更高频段(甚至高达0.8 Hz)持续存在,同时在扫描持续时间内呈现出变化较小的网络模式。然而,作为神经元活动的间接测量指标,BOLD信号源于本质上缓慢的血液动力学过程,实际上这一过程可能过于缓慢,无法适应所观察到的高频功能连接(FC)。为了检验观察到的高频自发FC是否源自BOLD对比度,我们收集了作为回波时间(TE)函数的RS数据。在这里,我们关注两个特定的静息态网络——默认模式网络(DMN)和执行控制网络(ECN),主要发现有四点:(1)我们在高达0.5 Hz的频段(TR = 1 s时可分辨的最大频率)的自发活动中观察到类似BOLD的线性TE依赖性,支持在更高频率范围内RSFC的神经相关性;(2)必须修改血液动力学响应函数的传统模型以支持静息态BOLD对比度,尤其是在更高频率时;(3)在传统的0.1 Hz以上,对RSFC的非BOLD样贡献比例增加(0.4 - 0.5 Hz时非BOLD/BOLD对比度约为<0.1 Hz时的4倍);(4)RSFC的空间模式是频率依赖性的。讨论了本研究结果背后的可能机制以及关于0.1 Hz以上RSFC的技术问题。

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本文引用的文献

1
Functional integration between brain regions at rest occurs in multiple-frequency bands.
Brain Connect. 2015 Feb;5(1):23-34. doi: 10.1089/brain.2013.0210. Epub 2014 Jun 25.
2
The effect of scan length on the reliability of resting-state fMRI connectivity estimates.
Neuroimage. 2013 Dec;83:550-8. doi: 10.1016/j.neuroimage.2013.05.099. Epub 2013 Jun 6.
3
Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest.
Front Hum Neurosci. 2013 May 1;7:168. doi: 10.3389/fnhum.2013.00168. eCollection 2013.
4
An Investigation of RSN Frequency Spectra Using Ultra-Fast Generalized Inverse Imaging.
Front Hum Neurosci. 2013 Apr 23;7:156. doi: 10.3389/fnhum.2013.00156. eCollection 2013.
5
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data.
Med Image Anal. 2013 Apr;17(3):365-74. doi: 10.1016/j.media.2013.01.003. Epub 2013 Jan 29.
6
EEG correlates of time-varying BOLD functional connectivity.
Neuroimage. 2013 May 15;72:227-36. doi: 10.1016/j.neuroimage.2013.01.049. Epub 2013 Jan 31.
7
Tracking dynamic resting-state networks at higher frequencies using MR-encephalography.
Neuroimage. 2013 Jan 15;65:216-22. doi: 10.1016/j.neuroimage.2012.10.015. Epub 2012 Oct 13.
8
Finding thalamic BOLD correlates to posterior alpha EEG.
Neuroimage. 2012 Nov 15;63(3):1060-9. doi: 10.1016/j.neuroimage.2012.08.025. Epub 2012 Aug 17.
9
The restless brain.
Brain Connect. 2011;1(1):3-12. doi: 10.1089/brain.2011.0019.
10
Spatiotemporal dynamics of the brain at rest--exploring EEG microstates as electrophysiological signatures of BOLD resting state networks.
Neuroimage. 2012 May 1;60(4):2062-72. doi: 10.1016/j.neuroimage.2012.02.031. Epub 2012 Feb 22.

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