Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS).
Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS).
复制标题
在渐进式延迟（Riptide）处理并发fMRI和近红外光谱（NIRS）时，使用回归插值（RIPTIDE）处理时使用回归插值来对BOLD FMRI数据进行生理降解。
DOI：
10.1016/j.neuroimage.2012.01.140
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发表时间：
2012-04-15
期刊：
影响因子：
5.7
通讯作者：
Tong, Yunjie
中科院分区：
文献类型：
--
作者：
Frederick, Blaise deB.;Nickerson, Lisa D.;Tong, Yunjie
关键词：
BOLD功能磁共振数据中的混杂噪声主要来自心脏和呼吸效应引起的血流和氧合的波动、动脉压的自发低频振荡(LFO)以及非任务相关的神经活动。心脏噪声尤其有问题，因为BOLD功能磁共振成像的低采样频率确保了这些影响在记录的数据中被混叠。已经提出了各种方法，通过测量和转换心脏和呼吸波形(例如，RETROICOR和每时间的呼吸量(RVT))来估计噪声信号，并通过检查空间和时间模式来无模型地估计噪声方差。我们之前已经证明，通过将体素特定的时间延迟应用于同时获取的近红外光谱(NIRS)数据，我们可以生成反映全身血流和氧合波动影响的回归变量。在这里，我们将这种方法应用于从粗略数据中去除生理噪声的任务。我们比较了由近红外光谱数据生成的各种噪声回归组的噪声去除效果，并将噪声去除与RETROICOR+RVT进行了比较。我们比较了利用原始数据和去噪数据进行静息状态分析的结果，并通过从静息数据计算零分布并将其与预期的理论分布进行比较来评估不同噪声滤波方法的偏差。使用最佳处理选择集，六个具有体素特定时间延迟的NIRS生成的回归变量解释了整个大脑10.5%的中位数方差，其中灰质的减少最高。相比之下，9个RETROICOR+RVT回归变量加在一起可以解释粗体数据中6.8%的中位数方差。NIRS去噪增强了对静止态网络的检测，不同技术的偏差没有明显差异。利用累进时间延迟(RIPTID)的Regressor插值法生成的生理性噪声回归量为有效去除BOLD数据中的血流动力学噪声提供了一种有效的方法。
Confounding noise in BOLD fMRI data arises primarily from fluctuations in blood flow and oxygenation due to cardiac and respiratory effects, spontaneous low frequency oscillations (LFO) in arterial pressure, and non-task related neural activity. Cardiac noise is particularly problematic, as the low sampling frequency of BOLD fMRI ensures that these effects are aliased in recorded data. Various methods have been proposed to estimate the noise signal through measurement and transformation of the cardiac and respiratory waveforms (e.g. RETROICOR and respiration volume per time (RVT)) and model-free estimation of noise variance through examination of spatial and temporal patterns. We have previously demonstrated that by applying a voxel-specific time delay to concurrently acquired near infrared spectroscopy (NIRS) data, we can generate regressors that reflect systemic blood flow and oxygenation fluctuations effects. Here, we apply this method to the task of removing physiological noise from BOLD data. We compare the efficacy of noise removal using various sets of noise regressors generated from NIRS data, and also compare the noise removal to RETROICOR+RVT. We compare the results of resting state analyses using the original and noise filtered data, and we evaluate the bias for the different noise filtration methods by computing null distributions from the resting data and comparing them with the expected theoretical distributions. Using the best set of processing choices, six NIRS-generated regressors with voxel-specific time delays explain a median of 10.5% of the variance throughout the brain, with the highest reductions being seen in gray matter. By comparison, the nine RETROICOR+RVT regressors together explain a median of 6.8% of the variance in the BOLD data. Detection of resting state networks was enhanced with NIRS denoising, and there were no appreciable differences in the bias of the different techniques. Physiological noise regressors generated using Regressor Interpolation at Progressive Time Delays (RIPTiDe) offer an effective method for efficiently removing hemodynamic noise from BOLD data.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
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通讯作者：
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影响因子：
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影响因子：
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作者：
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通讯作者：
Frederick, Blaise deB.
影响因子：
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作者：
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