Effects of different smoothing on global and regional resting functional connectivity.
Purpose: 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...
| Publicado en: | Neuroradiology Vol. 63; no. 1; pp. 99 - 110 |
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| Autor principal: | |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148073670&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148073670 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jan2021 vid: 63 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148073670 145294504 148073670 148073670 10.1007/s00234-020-02523-8 148073670 ppf: 99 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effects of different smoothing on global and regional resting functional connectivity. aug: au: Alahmadi, Adnan A. S. affil: Department of Radiologic Sciences, College of Applied Medical Science, King Abdulaziz University (KAU), Jeddah, Saudi Arabia sug: subj: Magnetic Resonance Imaging Methods Functional Connectivity Image Enhancement Methods Human Cross Sectional Studies Descriptive Statistics Data Analysis Funding Source ab: Purpose: 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. Methods: Nineteen healthy volunteers were scanned using rsfMRI with three applied smoothing kernels: 0 mm, 4 mm and 8 mm. Appropriate statistical comparisons were made. Results: 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. Conclusion: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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