Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data

Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data
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DOI:
10.1016/j.neuroimage.2012.09.043
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发表时间:
2013-01-01
期刊:
影响因子:
5.7
通讯作者:
Murphy, Kevin
Murphy, Kevin
中科院分区:
医学1区
文献类型:
--
作者:
Bright, Molly G.;Murphy, Kevin

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研究人群中不同的噪声方差已被证明会导致功能连接测量中的人为组差异。在这项研究中,我们调查使用短回波时间功能磁共振成像数据,以纠正这些噪声源的血氧水平依赖(BOLD)加权时间序列。使用双回波序列在短回波时间(TE= 3.3 ms)和BOLD加权回波时间(TE = 35 ms)同时采集数据。这种方法实际上是“自由的”,使用脉冲序列中的死区时间来收集额外的回波,而不影响整体扫描时间或时间分辨率。所提出的校正方法使用BOLD加权数据的短TE数据的逐体素回归来去除噪声方差。除了典型的静息状态扫描外,还通过增加10名受试者的头部运动或生理波动来模拟与患者组相关的不合规行为。短TE数据显示出显着的相关性与传统的运动相关和生理噪声回归用于当前的连接分析。在传统的预处理之后,由短TE数据回归因子解释的显著附加方差的程度与静息数据中扫描期间的平均头部运动显著相关(r(2)= 0.93,p
Differing noise variance across study populations has been shown to cause artifactual group differences in functional connectivity measures. In this study, we investigate the use of short echo time functional MRI data to correct for these noise sources in blood oxygenation level dependent (BOLD)-weighted time series. A dual-echo sequence was used to simultaneously acquire data at both a short (TE= 3.3 ms) and a BOLD-weighted (TE = 35 ms) echo time. This approach is effectively "free," using dead-time in the pulse sequence to collect an additional echo without affecting overall scan time or temporal resolution. The proposed correction method uses voxelwise regression of the short TE data from the BOLD-weighted data to remove noise variance. In addition to a typical resting state scan, non-compliant behavior associated with patient groups was simulated via increased head motion or physiological fluctuations in 10 subjects. Short TE data showed significant correlations with the traditional motion-related and physiological noise regressors used in current connectivity analyses. Following traditional preprocessing, the extent of significant additional variance explained by the short TE data regressors was significantly correlated with the average head motion across the scan in the resting data (r(2) = 0.93, p