Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Laarbeeklaan 101, 1090 Brussels, Belgium.
Faculty of Medicine and Health Sciences, Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium.
Biomed Phys Eng Express. 2022 Jul 1;8(5). doi: 10.1088/2057-1976/ac63f0.
One of the main challenges in fMRI processing is filtering the task BOLD signals from the noise. Independent component analysis with automatic removal of motion artifacts (ICA-AROMA) reduces motion artifacts by identifying ICA noise components based on their location at the brain edges and cerebrospinal fluid (CSF), high frequency content and correlation with motion regressors. In anatomical component correction (aCompCor), physiological noise regressors extracted from CSF were regressed out from the fMRI time series. In this study, we compared three methods to combine aCompCor and ICA-AROMA denoising in one denoising step. In the first analysis, we regressed the temporal signals of the ICA components identified as noise by ICA-AROMA together with the noise signals determined by aCompCor from the fMRI signals. For the second and third analyses, the correlation between the temporal signals of the ICA components and the aCompCor noise signals was used as an additional criterion to identify the noise components. In the second analysis, the temporal signals of the ICA components classified as noise were regressed from the fMRI signals. In the third analysis, the noise components were removed. To compare the denoising strategies, we examined the fractional amplitude of low-frequency fluctuations (fALFF) and the overlap between the contrast maps. Our results revealed that including the aCompCor noise signals as regressors in ICA-AROMA resulted in more correctly identified noise components, higher fALFF values, and larger activation maps. Moreover, combining the temporal signals of the noise components identified by ICA-AROMA with the aCompCor signals in a noise regression matrix resulted in deactivations. These results suggest that using the correlation between the ICA component temporal signals and the aCompCor signals as noise identification criteria in ICA-AROMA is the best approach for combining both denoising methods.
功能磁共振成像处理中的一个主要挑战是从噪声中过滤任务大脑血氧水平依赖信号。独立成分分析与自动去除运动伪影（ICA-AROMA）通过基于其在脑边缘和脑脊髓液（CSF）、高频内容和与运动回归器的相关性的位置来识别 ICA 噪声成分，从而减少运动伪影。在解剖成分校正（aCompCor）中，从 CSF 中提取的生理噪声回归器从 fMRI 时间序列中回归。在这项研究中，我们比较了三种方法来在一个去噪步骤中组合 aCompCor 和 ICA-AROMA 去噪。在第一个分析中，我们将 ICA-AROMA 确定为噪声的 ICA 成分的时间信号与 aCompCor 确定的来自 fMRI 信号的噪声信号一起回归。对于第二和第三分析，ICA 成分的时间信号与 aCompCor 噪声信号之间的相关性被用作识别噪声成分的附加标准。在第二个分析中，从 fMRI 信号中回归 ICA 成分分类为噪声的时间信号。在第三个分析中，去除噪声成分。为了比较去噪策略，我们检查了低频波动的分数幅度（fALFF）和对比图之间的重叠。我们的结果表明，将 aCompCor 噪声信号作为 ICA-AROMA 中的回归器包括在内，会导致更多正确识别的噪声成分、更高的 fALFF 值和更大的激活图。此外，将 ICA-AROMA 中确定的噪声成分的时间信号与 aCompCor 信号组合在噪声回归矩阵中，会导致去激活。这些结果表明，在 ICA-AROMA 中使用 ICA 成分时间信号与 aCompCor 信号之间的相关性作为噪声识别标准是结合这两种去噪方法的最佳方法。
