The effect of respiration variations on independent component analysis results of resting state functional connectivity
The effect of respiration variations on independent component analysis results of resting state functional connectivity
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DOI：
10.1002/hbm.20577
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发表时间：
2008-07-01
影响因子：
4.8
通讯作者：
Bandettini, Peter A.
中科院分区：
文献类型：
--
作者：
Birn, Rasmus M.;Murphy, Kevin;Bandettini, Peter A.
功能性磁共振成像（fMRI）中功能连接性的分析可能会受到心脏和呼吸波动的严重影响。虽然通过生理噪声校正程序可以减少一些这种人为的信号变化，但由呼吸深度和频率的逐次呼吸变化所引起的较慢的信号波动通常无法消除。这些由呼吸引起的较慢的信号变化发生在低频段，且其空间位置与用于推断功能连接性的波动相似，并且已表明会显著影响基于种子感兴趣区（seed - ROI）或种子体素（seed - voxel）的功能连接性分析，特别是在默认模式网络中。在本研究中，我们研究了呼吸变化对从静息态数据的独立成分分析（ICA）得出的功能连接图的影响。默认模式网络的区域是通过词汇判断任务期间的去激活来确定的。呼吸的变化被独立测量，并与磁共振成像（MRI）时间序列数据相关联。在大多数情况下，独立成分分析似乎能将默认模式网络和与呼吸相关的变化区分开来。然而，在某些情况下，自动被识别为默认模式网络的成分与被识别为与呼吸相关的成分是相同的。此外，在大多数情况下，与默认模式网络成分相关的时间序列仍然与单位时间呼吸量的变化显著相关，这表明当前的独立成分分析方法可能无法将呼吸与默认模式网络完全分离。对呼吸的独立测量提供了有价值的信息，有助于将默认模式网络与呼吸相关的信号变化区分开来，并评估残余呼吸相关效应的程度。
The analysis of functional connectivity in fMRI can be severely affected by cardiac and respiratory fluctuations. While some of these artifactual signal changes can be reduced by physiological noise correction routines, signal fluctuations induced by slower breath-to-breath changes in the depth and rate of breathing are typically not removed. These slower respiration-induced signal changes occur at low frequencies and spatial locations similar to the fluctuations used to infer functional connectivity, and have been shown to significantly affect seed-ROI or seed-voxel based functional connectivity analysis, particularly in the default mode network. In this study, we investigate the effect of respiration variations on functional connectivity maps derived from independent component analysis (ICA) of resting-state data. Regions of the default mode network were identified by deactivations during a lexical decision task. Variations in respiration were measured independently and correlated with the MRI time series data. ICA appears to separate the default mode network and the respiration-related changes in most cases. In some cases, however, the component automatically identified as the default mode network was the same as the component identified as respiration-related. Furthermore, in most cases the time series associated with the default mode network component was still significantly correlated with changes in respiration volume per time, suggesting that current methods of ICA may not completely separate respiration from the default mode network. An independent measure of the respiration provides valuable information to help distinguish the default mode network from respiration-related signal changes, and to assess the degree of residual respiration related effects.