Department of Radiologic Sciences, College of Applied Medical Science, King Abdulaziz University (KAU), Jeddah, Saudi Arabia.
Neuroradiology. 2021 Jan;63(1):99-109. doi: 10.1007/s00234-020-02523-8. Epub 2020 Aug 25.
Spatial smoothing is an essential pre-processing step in the process of analysing functional magnetic resonance imaging (fMRI) data, both during an experimental task or during resting-state fMRI (rsfMRI). The main benefit of this spatial smoothing step is to artificially increase the signal-to-noise ratio of the fMRI signal. Previous fMRI studies have investigated the impact of spatial smoothing on task fMRI data, while rsfMRI studies usually apply the same analytical process used for the task data. However, this study investigates changes in different rsfMRI analyses, such as ROI-to-ROI, seed-to-voxels and ICA analyses.
Nineteen healthy volunteers were scanned using rsfMRI with three applied smoothing kernels: 0 mm, 4 mm and 8 mm. Appropriate statistical comparisons were made.
The findings showed that spatial smoothing has a greater effect on rsfMRI data when analysed using seed-to-voxel-based analysis. The effect was less pronounced when analysing data using ROI-ROI or ICA analyses. The results demonstrated that even when analysing the data without the application of spatial smoothing, the results were significant compared with data analysed using a typical smoothing kernel. However, data analysed with lower-smoothing kernels produced greater negative correlations, particularly with the ICA analysis.
The results suggest that a medium smoothing kernel (around 4 mm) may be preferable, as it is comparable with the 8 mm kernel in all of the analyses performed. It is also recommended that the researchers consider analysing the data using two different smoothing kernels, as this will help to confirm the significance of the results and avoid overestimating the findings.
空间平滑是分析功能磁共振成像（fMRI）数据的一个基本预处理步骤，无论是在实验任务期间还是在静息态 fMRI（rsfMRI）期间。该空间平滑步骤的主要好处是人为地提高 fMRI 信号的信噪比。以前的 fMRI 研究已经研究了空间平滑对任务 fMRI 数据的影响，而 rsfMRI 研究通常应用与任务数据相同的分析过程。然而，本研究调查了不同 rsfMRI 分析的变化，如 ROI-ROI、种子到体素和 ICA 分析。
19 名健康志愿者使用 rsfMRI 进行扫描，应用了三种平滑核：0mm、4mm 和 8mm。进行了适当的统计比较。
研究结果表明，当使用基于种子到体素的分析方法对 rsfMRI 数据进行分析时，空间平滑对数据的影响更大。当使用 ROI-ROI 或 ICA 分析方法分析数据时，效果不太明显。结果表明，即使在不应用空间平滑的情况下分析数据，与使用典型平滑核分析相比，结果仍然具有统计学意义。然而，使用较低平滑核分析的数据产生了更大的负相关，特别是与 ICA 分析。
结果表明，中等平滑核（约 4mm）可能更可取，因为它在所有执行的分析中与 8mm 核相当。还建议研究人员考虑使用两种不同的平滑核来分析数据，因为这有助于确认结果的显著性并避免高估发现。
