Section on Functional Imaging Methods, Functional MRI Core Facility, and Statistical and Scientific Computing Core, National Institute of Mental Health, Bethesda, MD 20814.
Proc Natl Acad Sci U S A. 2013 Oct 1;110(40):16187-92. doi: 10.1073/pnas.1301725110. Epub 2013 Sep 13.
Functional connectivity analysis of resting state blood oxygen level-dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (≤1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals-a characteristic property of BOLD T*2 signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.
静息态血氧水平依赖 (BOLD) 功能磁共振的功能连接分析广泛用于无创性研究大脑功能网络。然而，最近的研究结果表明，即使在扫描过程中头部有很小的（≤1mm）运动，也会不成比例地影响连接估计值，尽管进行了各种预处理。从时域信号估计区域间连接的进一步复杂性包括与高对象运动引起的灵敏度损失相关的 BOLD 自由度的未被计算的减少。为了解决这些问题，我们描述了一种用于数据采集、去噪和连接估计的集成策略。该策略建立在我们之前发表的技术基础上，该技术将数据采集与多回波 (ME) 回波平面成像以及基于信号线性回波时间依赖性的空间独立成分分析 (ICA) 分析相结合，称为 ME-ICA，该技术根据信号的线性回波时间依赖性区分 BOLD（神经元）和非 BOLD（人为）成分，这是 BOLD T*2 信号变化的特征属性。在这里，我们为 32 个对照受试者展示了该方法如何提供一种基于物理原理且几乎不受操作员影响的方法来从静息状态数据中去除复杂的伪影，例如运动。然后，我们描述了一种基于 BOLD 独立成分系数的区域间相关性的稳健功能连接估计器。该估计器称为独立成分回归，它极大地简化了功能连接的统计推断，因为自由度等于独立系数的数量。与传统的连接估计方法相比，所提出的策略可使信噪比提高四倍，特异性更高的功能连接分析，以及在具有不同运动水平的组之间的连接对比度中具有名义控制类型 1错误的有效统计推断。
