Slice-timing effects and their correction in functional MRI

By R. Sladky, Karl J. Friston, J. Tröstl, Ross Cunnington, E. Moser, C. Windischberger. NeuroImage, 2011

449 citations recorded by the source corpus.

Exact timing is essential for functional MRI data analysis. Datasets are commonly measured using repeated 2D imaging methods, resulting in a temporal offset between slices. To compensate for this timing difference, slice-timing correction (i.e. temporal data interpolation) has been used as an fMRI pre-processing step for more than fifteen years. However, there has been an ongoing debate about the effectiveness and applicability of this method. This paper presents the first elaborated analysis of the impact of the slice-timing effect on simulated data for different fMRI paradigms and measurement parameters, taking into account data noise and smoothing effects. Here we show, depending on repetition time and paradigm design, slice-timing effects can significantly impair fMRI results and slice-timing correction methods can successfully compensate for these effects and therefore increase the robustness of the data analysis. In addition, our results from simulated data were supported by empirical in vivo datasets. Our findings suggest that slice-timing correction should be included in the fMRI pre-processing pipeline.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b62648c3948552db796db6d5e0495fa93b12ee5e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Jinhui Wang
2. Yong He
3. Alan C. Evans
4. Mingrui Xia
5. Xuhong Liao
6. Xindi Wang
7. Joshua Carp
8. Scott A. Huettel

In Radiology, Nuclear Medicine and Imaging

1. Jinhui Wang
2. Yong He
3. Alan C. Evans
4. Mingrui Xia
5. Xuhong Liao
6. Xindi Wang
7. Joshua Carp
8. Scott A. Huettel

Citations in the corpus

• The first step for neuroimaging data analysis: DICOM to NIfTI conversion. (2016, unknown): “This process has been shown to improve statistical power for typical MRI sampling rates (Sladky et al., 2011).”
• GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, background): “The slice-timing effects have been demonstrated to have prominent effects on study results and can be successfully compensated by the slice timing correction step (i.e., temporal data interpolation; Sladky et al., 2011).”
• The secret lives of experiments: Methods reporting in the fMRI literature (2012, unknown): “Previous research has demonstrated the impact of slice timing correction (Sladky et al., 2011), normalization (Klein et al.”
• Precuneus Is a Functional Core of the Default-Mode Network (2014, methodology): “Next, we corrected intravolume slicetiming differences using Fourier-space phase shifting, aligning to the middle slice (Sladky et al., 2011).”
• Methods for cleaning the BOLD fMRI signal (2016, unknown): “Even though slice timing correction is often thought to be unnecessary with short TR or introduce inaccuracies in temporal interpolation with long TRs, it does not alter the analysis results in the worst case, and often improve them for event related designs (Kiebel et al., 2007; Sladky et al., 2011”
• On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments (2012, background): “For example, Sladky et al. (2011) argue that studies should perform slice-timing correction (but see also Poldrack et al., 2011, pp. 41–42); Purdon and Weisskoff (1998) suggest that studies should correct for temporal autocorrelation; and Lund et al. (2005) argue that studies should include head…”