Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD 20892, USA.
Neuroimage. 2012 Apr 15;60(3):1759-70. doi: 10.1016/j.neuroimage.2011.12.028. Epub 2011 Dec 23.
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 R(2)* and initial signal intensity (S(0)) 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 R(2)* and S(0) 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.
在基于 fMRI 的功能连接研究中，一个核心挑战是区分与神经元相关的信号波动与运动、生理和其他杂散源的影响。去除杂散效应的传统技术包括基于外部测量的噪声时程建模，然后进行时间滤波。这些技术的效果有限。先前的研究表明，使用多回波 fMRI，可以根据回声时间（TE）依赖性的分析，用 R(2)*和初始信号强度（S(0)）的变化来描述与神经元相关的波动是血氧水平依赖（BOLD）信号。我们假设如果可以使用 TE 依赖性来区分 BOLD 和非 BOLD 信号，则可以在不进行传统噪声建模的情况下，通过去除非 BOLD 信号来对数据进行降噪。为了验证这一假设，在 3 个 TE 处采集了全脑多回波数据，并在跨空间和 TE 空间上对数据进行空间连接后，使用独立成分分析（ICA）进行分解。对成分的信号变化与 R(2)*和 S(0)变化模型的拟合程度进行了分析，并开发了综合评分来描述每个成分是否为 BOLD 样或非 BOLD 样。这些评分清楚地区分了 BOLD 样“功能网络”成分与与运动、脉动和其他杂散效应相关的非 BOLD 样成分。使用非 BOLD 样成分时程作为噪声回归器，可以通过减少高频和低频非 BOLD 波动的影响，极大地改善基于种子的相关映射。与使用传统噪声回归器的基于种子的相关映射比较表明，该技术对于个体和组水平基于种子的连通性分析都具有优越性，特别是在映射皮质下-皮质连接方面。基于 TE 依赖性的 BOLD 和非 BOLD 成分的区分具有高度的稳健性，这允许识别 BOLD 样成分并去除非 BOLD 样成分，从而实现完全自动化的处理。
