Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI.
Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI.
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DOI：
10.1016/j.neuroimage.2011.12.028
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发表时间：
2012-04-15
期刊：
影响因子：
5.7
通讯作者：
Bandettini, Peter A.
中科院分区：
文献类型：
--
作者：
Kundu, Prantik;Inati, Souheil J.;Evans, Jennifer W.;Luh, Wen-Ming;Bandettini, Peter A.
基于功能磁共振成像的功能连接研究的一个核心挑战是区分神经元相关的信号波动与运动，生理和其他干扰源的影响。用于去除滋扰效应的常规技术包括基于外部测量对噪声时间过程进行建模，随后进行时间滤波。这些技术的有效性有限。先前的研究表明，使用多回波功能磁共振成像，神经元相关的波动是血氧水平依赖（BOLD）的信号，可以根据回波时间（TE）依赖性的分析，在R2* 和初始信号强度（S0）的变化来表征。我们假设，如果TE依赖性可以用来区分BOLD和非BOLD信号，非BOLD信号可以被去除去噪数据，而无需传统的噪声建模。为了验证这一假设，在3个TE处采集全脑多回波数据，并在空间上连接空间和TE上的数据后用独立分量分析（伊卡）进行分解。分析组分的信号变化与R2* 和S0变化模型的拟合程度，并开发总结评分以将每个组分表征为BOLD样或非BOLD样。这些分数清楚地区分了BOLD样“功能网络”组件与与运动、脉动和其他滋扰效应相关的非BOLD样组件。使用非BOLD类分量时间过程作为噪声回归量，通过减少高频和低频非BOLD波动的影响，显著改善了基于种子的相关映射。与种子为基础的相关映射使用传统的噪声回归的比较表明，所提出的技术的优越性，为个人和群体水平的种子为基础的连接性分析，特别是在映射皮层下皮层连接。基于TE依赖性的BOLD和非BOLD组分的区分是高度稳健的，这允许将BOLD样组分的识别和非BOLD样组分的去除作为全自动程序来实施。
A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R2* and initial signal intensity (S0) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R2* and S0 change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like “functional network” components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.
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影响因子：
5.7
作者：
Beissner, Florian;Baudrexel, Simon;Deichmann, Ralf
通讯作者：
Deichmann, Ralf
影响因子：
3.3
作者：
Krüger, G;Glover, GH
通讯作者：
Glover, GH
影响因子：
2.5
作者：
Barth, M;Reichenbach, JR;Haacke, EM
通讯作者：
Haacke, EM
影响因子：
3.7
作者：
Feinberg DA;Moeller S;Smith SM;Auerbach E;Ramanna S;Gunther M;Glasser MF;Miller KL;Ugurbil K;Yacoub E
通讯作者：
Yacoub E
DOI：
10.1073/pnas.0601417103
发表时间：
2006-09-12
影响因子：
11.1
作者：
Damoiseaux, J. S.;Rombouts, S. A. R. B.;Beckmann, C. F.
通讯作者：
Beckmann, C. F.